Topic evolution analysis in digital rural policies of China based on LDA model

Topic evolution analysis in digital rural policies of China based on LDA model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Topic evolution analysis in digital rural policies of China based on LDA model Cuicui Wang, Kai HU, Bo CAI, Huiping Wu, Wei Luo, Ronghua Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6689009/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract With the continuous advancement of information technology, digital innovation has emerged as a pivotal driver of rural revitalization and agricultural modernization in China. This study analyzes 88 digital rural policy documents issued at the central government level between 2017 and 2024. Using the Latent Dirichlet Allocation (LDA) topic modeling approach, we identify nine key thematic areas: agricultural mechanization, e-commerce logistics, digital villages, digital commerce, rural governance, industrial finance, poverty alleviation, green agriculture, and rural reform, further analysis of its evolution trend found that: First, the policy topics exhibit broad coverage, spanning diverse dimensions such as agricultural production, e-commerce logistics, and rural governance. Second, thematic differentiation is evident: seven themes—including agricultural mechanization, e-commerce logistics, and digital villages—demonstrate sustained growth or stability, whereas green agriculture and rural reform show a declining trend. Third, the thematic focus of China’s digital rural policies has evolved from traditional components such as mechanization toward digitally driven transformation, exemplified by the rise of smart agriculture. Based on these insights, we propose targeted recommendations to enhance inter-agency coordination in policy formulation and reinforce policy priorities' alignment with core digital technologies. Social science/Economics Social science/Social policy Digital village LDA model Theme evolution Theme mining Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction In the context of accelerating globalization and digitalization, digital technology has emerged as a pivotal force in driving socioeconomic development. It is progressively reshaping the operational landscape of multiple sectors and exerting a profound influence on urban–rural development model. In 2017, China officially proposed leveraging information technologies to improve rural infrastructure and enhance the living standards of farmers. In 2019, the General Office of the CPC Central Committee and the State Council jointly issued the Strategic Plan for Digital Village Development, outlining a national vision for building digital villages (Wu & Zeng, 2022 ).The 20th National Congress of the Communist Party of China emphasized the importance of advancing national rejuvenation through Chinese-style modernization, promoting the development of a strong cyber power and Digital China, and accelerating rural revitalization and the construction of an agricultural powerhouse. These measures are part of China 's long-term development strategy (Zheng, 2022 ). In 2025, China proposed the implementation of a Special Initiative for Strengthening, Benefiting, and Enriching Agriculture through Digital village Development. In recent years, China has introduced a series of policies to promote the construction of digital villages, providing strategic guidance for their development and effectively advancing the nationwide digital village initiative. Analyzing the thematic features and evolutionary trends of China's digital village policy over the years is of great significance for further optimizing the policy framework. 2 Literature Review Existing research on digital village policy primarily focuses on four key areas:① The strategic significance of digital villages for rural revitalization. Zhao et al. ( 2022 ) argue that digital villages play a vital strategic role in addressing the "three rural issues" (agriculture, rural areas, and farmers) and strengthening rural governance systems, offering new pathways to overcome longstanding challenges in rural revitalization. Similarly, Snowball et al. (2021) suggest that the development of digital villages aligns with the broader digitalization era, injecting new vitality into rural development and unlocking "digital dividends" by effectively leveraging emerging technologies for value creation and acquisition.② Mechanisms through which digital villages promote rural revitalization. Liu et al. ( 2022 ) systematically explored the mechanisms by which digital villages facilitate rural revitalization from a theoretical perspective and validated—through empirical analysis—that digitalization of the rural economy and rural lifestyles are dual pathways empowering such revitalization. Building on a review of relevant literature, Rolandi et al. ( 2021 ) emphasized that the digital transformation of agriculture and rural areas will trigger profound changes across four dimensions: economy, environment, governance, and society.③ Evaluation and measurement of digital village policies. Ruiz et al. (2011) proposed the "Omnia Mobilis" hypothesis and introduced the PMC index model to assess digital village policies. Duan et al. ( 2023 ) applied the entropy method and a coupling coordination model to quantitatively measure the coordination between central and local digital village policies and explored the effects of multi-level policy linkages. By drawing on grounded theory, Liu & Wei ( 2024 ) constructed a policy tool selection framework for digital village construction, analyzing preferences and combinations in the use of policy instruments.④ Evaluation methods for policy texts. Liu et al. ( 2021 ) established a three-dimensional framework for policy analysis and adopted strategies such as social network analysis and the PMC index model to examine collaboration networks among actors, policy content, and implementation effectiveness during public health emergencies. Chen & Duan ( 2020 ) utilized TF-IDF and LDA models to identify guarantees and policy objectives within implementation processes, constructing an implementation framework for open data policies. Cole et al. ( 2019 ) employed a difference-in-differences (DID) model combined with inverse probability of treatment weighting to compare the effects of primary healthcare policies on treated versus control groups of children. Lin et al. ( 2023 ) conducted a text analysis of U.S. national public health and healthcare policy documents and cases released between 2000 and 2022. These prior studies provide a solid theoretical foundation for understanding the role and trajectory of digital village policies. However, few studies have systematically investigated the thematic evolution of such policies via policy text analysis. This paper applies text mining techniques, particularly the Latent Dirichlet Allocation (LDA) model, to uncover latent thematic structures within policy documents to fill this gap. It further investigates the evolution of digital village policy themes and proposes optimization strategies, offering a novel perspective for advancing the development of China's digital village policy system. 3 Selection and External Feature Analysis of Digital Village Policy Texts 3.1 Selection of Digital Village Policy Texts To ensure analytical consistency and institutional authority, this study focuses on digital village policy documents issued by the Central Committee of the Communist Party of China, the State Council, and key national ministries. Using keywords such as “digital village,” “digital governance,” “agricultural informatization,” “smart agriculture,” “Agricultural and Rural Modernization,” and “digital life,” a total of 88 relevant documents published between 2017 and 2024 were retrieved from official government portals and authoritative databases, including PKUlaw (Beida Fabao). 3.2 Descriptive Overview of the Digital Village Policy Corpus 3.2.1 Classification of Policy Document Types Policy documents are typically classified into five categories: regulations, notices, opinions, plans, and strategic directives (Li & Qin, 2023 ). Quantitative analysis of the collected corpus (N = 88) reveals a distinct typological distribution shown in Table 1 . : opinion documents dominate (44.32%), followed by plans (21.59%), with notices (17.05%) and strategic plans (15.91%) constituting smaller proportions. Regulatory documents are exceptionally rare, comprising merely 1.14% of the total. Table 1 Typological distribution of digital village policy documents Category Frequency Proportion (%) Advisory Opinions 39 44.32 Developmental Plans 19 21.59 Administrative Notices 15 17.05 Strategic Directives 14 15.91 Regulations 1 1.14 3.2.2 Temporal Distribution of Digital Village Policy Documents Overall, the issuance of digital village policy documents in China has exhibited a steadily increasing trend over the years, as illustrated in Fig. 1 , reflecting the central government's growing emphasis on constructing digital villages. Notably, a decline in the number of policy releases was observed in 2024, which may be attributed to delays in publication or incomplete collection of policy texts due to temporal lags in official disclosure. 3.2.3 Institutional Analysis of Digital Village Policy Issuing Authorities From the perspective of issuing authorities, 43 policy documents (48.86%) were issued by a single agency, 24 documents (27.27%) were jointly issued by two agencies, and 21 documents (23.87%) were co-issued by three or more departments. Gephi-based network analysis reveals the following characteristics of the issuing authorities: First, the highest frequency of joint issuance (16 instances) occurred under the names of the Central Committee of the Communist Party of China and the State Council, indicating a high level of authority and strong central government emphasis. Second, policy issuance involved a broad range of institutions—35 ministries and commissions in total—participating in 232 instances of joint issuance, suggesting a high degree of interdepartmental collaboration and a proactive response to central directives. 4 Thematic Evolution of Digital Village Policies Based on the LDA Model 4.1 . 1 LDA topic model LDA, first proposed by Blei et al. in 2003 (Blei et al., 2003 ), is a three-layer Bayesian probabilistic model. Its core principle is that each document comprises a mixture of topics, and a distribution of words characterizes each topic. By leveraging unsupervised learning techniques, LDA enables the extraction of latent thematic structures from large-scale textual data, thereby uncovering hidden topic patterns within extensive document collections or corpora (Momtazi, 2018 ;Martinez & Kak, 2001 ). Policy documents are typically highly specialized and are often presented in unstructured formats (Wu et al., 2021 ). As a widely adopted text mining technique in recent years, the LDA topic model has proven effective in identifying underlying themes in policy texts and enhancing their interpretability. 4.2 Data Pre-processing Methods This study utilized the Jieba library in Python to perform word segmentation. Data Pre-processing was conducted by incorporating a user-defined lexicon, a stopword list, and a synonym dictionary. The user-defined lexicon primarily included domain-specific proper nouns closely related to digital village development, such as “smart agriculture” and “digital governance.” The synonym dictionary was used to normalize semantically equivalent terms with varying expressions, such as “rural e-commerce” and “rural electronic commerce.” Stopwords—function words with limited semantic value (e.g., prepositions)—were removed using the stop word list of Harbin Institute of Technology. Subsequently, word frequency statistics were generated, and a word cloud was constructed to visualize the most salient terms, as shown in Fig. 2 . Figure 2 provides an intuitive visualization of the keywords extracted from the policy documents and their relative importance. It exhibits the following characteristics:First, high-frequency terms such as “construction,” “development,” “services,” “agriculture,” “rural,” and “agricultural products” highlight the primary areas of focus in digital rural policy.Second, keywords like “technology,” “digital,” and “innovation” maintain substantial weight, indicating the prominent role of digital technologies in policy design.Third, terms such as “organization,” “management,” and “improvement” suggest that the policies emphasize not only technical and infrastructural development but also the enhancement of institutional systems in rural governance. 4.3 Optimal Number of Topics Determination Topic modeling was conducted using the LDA algorithm implemented via the Gensim library in Python. The model was trained with default values for the hyperparameters α and β, and the number of iterations was set to 500. The methods for determining the optimal number of topics include the perplexity model, the topic coherence model, and visual inspection using pyLDAvis (Feng et al., 2024 ). This study adopts the perplexity model and supplements it with pyLDAvis-based visualization of clustering results to identify the optimal number of topics. As illustrated by the perplexity curve in Fig. 3 , the model achieves its lowest perplexity when the number of topics reaches nine, aligning with the "elbow" method commonly used to identify the optimal point (Zhang et al., 2021 ). This indicates that the model's explanatory power is maximized at this topic count. Furthermore, the pyLDAvis visualization in Fig. 4 demonstrates that when the number of topics is set to nine, the topic clusters are evenly distributed, with no overly dominant or insignificant bubbles, suggesting a well-balanced and relatively independent topic structure. 4.4 Theme Extraction of Policy Text The high-probability term distributions for China’s digital village policy themes, derived through the LDA theme model,, are presented in Table 2 . A total of nine topics were extracted, with ten representative keywords listed for each topic. Based on the probabilistic distribution of characteristic terms for each topic (Ylä-Anttila et al., 2022 ), the nine themes were sequentially labeled as follows: Topic 1 (E-commerce Logistics), Topic 2 (Agricultural Mechanization), Topic 3 (Green Agriculture), Topic 4 (Rural Reform), Topic 5 (Rural Governance), Topic 6 (Industrial Finance), Topic 7 (Digital Countryside), Topic 8 (Digital Commerce), and Topic 9 (Targeted Poverty Alleviation). Table 2 Theme-word distribution Subject No Thematic Overview Top 10 High Probability Feature Words Topic1 E-commerce Logistics 0.012* Urban-Rural + 0.008* Countryside + 0.007* Logistics + 0.007* Judiciary + 0.007* Rural E-commerce + 0.007* Lawfully + 0.006* Protection + 0.006* Commerce + 0.005* Live streaming + 0.004* Governance Topic2 Agricultural Machinery 0.040* Mechanization + 0.038* Agricultural Machinery + 0.021* Primary Processing + 0.007* Technical Equipment + 0.006* Breeding + 0.006* R&D + 0.006* Informatization + 0.006* Efficiency + 0.005* Training + 0.005* Grading Topic3 Green Agriculture 0.018* Green + 0.009* Base + 0.008* Efficiency + 0.007* Farm + 0.006* Lead + 0.006* Breeding + 0.006* Agricultural prosperity + 0.006* Livestock and Poultry + 0.005* Cooperative + 0.004* R&D Topic4 Rural Reform 0.019* Countryside + 0.012* Rural Revitalization + 0.007* Protection + 0.007* Reform + 0.006* Entrepreneurship + 0.005* Culture + 0.005* Poverty Alleviation + 0.005* Ecology + 0.005* Cultivated Land + 0.004* Urban-Rural Topic5 Rural Governance 0.007* Farmers + 0.007* Protection + 0.007* Governance + 0.006* Reform + 0.006* Deepening + 0.006* Culture + 0.005* Strategy + 0.005* Ecology + 0.005* Region + 0.004* Consumption Topic6 Industrial Finance 0.011* Breeding + 0.008* Transformation + 0.007* Finance + 0.007* Planting + 0.006* Financing + 0.006* Supporting Facilities + 0.005* Grain + 0.005* Logistics + 0.005* Production Area + 0.005* Planning Topic7 Digital Village 0.017* Data + 0.016* Countryside + 0.014* Digitalization + 0.013* Digital + 0.013* Informatization + 0.011* Digital Village + 0.009* Smart + 0.008* Intelligent + 0.007* Internet + 0.006* System Topic8 Digital Commerce 0.035* Digital + 0.027* Data + 0.021* 5G + 0.016* Digitalization + 0.014* E-commerce + 0.009* Intelligent + 0.008* Smart + 0.007* Governance + 0.006* Meteorology + 0.006* Digital Transformation Topic9 Targeted Poverty Alleviation 0.030* Rural Revitalization + 0.021* Poverty Alleviation + 0.014* Countryside + 0.009* Tackling Key Problems + 0.008* Assistance + 0.008* Rural Governance + 0.006* Township + 0.006* Consolidation + 0.006* Party Committee + 0.005* Achievements 5 Thematic Analysis of Digital Village Policies This section systematically analyses policy texts from two dimensions—topic intensity and thematic evolution—to uncover shifting policy priorities across different stages of digital village development in China. The findings are intended to provide theoretical support for optimising the national digital village policy framework. 5.1 Topic Intensity Analysis To examine the evolutionary trajectories of thematic emphases, this study employs a post-discretization method (Shan & Li, 2010 ) to quantify annual topic intensities based on the output of the LDA model. A topic heatmap was constructed accordingly, where color saturation represents the relative strength of each topic over time, as shown in Fig. 5 . Thematically, digital Village policies have predominantly centered on areas such as rural reform (Topic 4), digital village (Topic 7), green agriculture (Topic 3), and digital commerce (Topic 8). Temporally, there is a discernible shift in policy focus from rural reform (Topic 4) to digital village (Topic 7), suggesting a strategic transition from broad structural reform to more targeted and technologically oriented digital infrastructure development. 5.2 Topic Evolution Trend Analysis As China’s Digital Village Strategy advances, policy priorities have undergone phased adjustments. Systematic analysis of these evolutionary patterns is instrumental in capturing broader policy dynamics and informing future optimization. Based on temporal variations in topic intensity, the nine identified themes are categorized into three groups: ascent, descent, and stability. 5.2.1 Analysis of the rising trend of digital village policy topics As can be seen from Fig. 6 , Topic 8 (Digital Commerce), Topic 7 (Digital Village), and Topic 1 (E-commerce Logistics) exhibited a sustained upward trajectory between 2017 and 2024. (1)Topic 8 (Digital Commerce) has exhibited a consistently rising trend in thematic intensity since 2019, underscoring its increasing policy relevance within China’s digital village development agenda. The rapid proliferation of rural e-commerce platforms has effectively shortened the supply chain between agricultural producers and end consumers, thereby lowering transaction and distribution costs across the agricultural value chain. By leveraging digital platforms, consumers gain access to geographically dispersed, high-quality agricultural products, thus helping to resolve the dual bottlenecks of “sales difficulty” on the supply side and “ purchase difficulty” on the demand side. These dynamics have not only enhanced rural household incomes but also improved farmers’ digital literacy and technology adoption capacity, further accelerating the construction of the digital countryside (Han & Gong, 2025 ). Amid the ongoing expansion of the digital economy, numerous rural regions have mobilized endogenous resources and cultivated localized specialty industries through e-commerce platforms. This has led to the emergence of digitally enabled commercial clusters such as “Taobao Villages” and “Taobao Towns,” which have become focal nodes of rural digital commerce. These developments demonstrate the dual economic and social dividends generated by rural e-commerce and affirm its strategic role as a core pillar in China’s broader digital countryside initiative. (2)Topic 7 (Digital Village) ranks among the most salient themes, exhibiting a generally upward but fluctuating trajectory in intensity, with a marked increase observed in 2024. The construction of digital countryside has emerged as a critical policy initiative, formulated in response to the structural realities of rural development in China (Gu, 2023 ). It not only provides robust support for the implementation of the rural revitalization strategy but also increasingly serves as a strategic conduit linking rural development goals with the broader national agenda of “Digital China.” Enabled by modern information technologies, digital countryside initiatives are catalyzing transformative changes across multiple dimensions—including agricultural production, rural livelihoods, and governance systems (Yan, 2024 ). (3)Topic 1 (E-commerce Logistics) has exhibited a gradual upward trend in thematic intensity over the examined period. As a pivotal component of digital commerce, e-commerce logistics has benefited significantly from digital technology, contributing to the steady enhancement of rural e-commerce infrastructure, logistics networks, and the digitization of agricultural product distribution. Anchored in the principle of shared logistics resources, rural e-commerce logistics is increasingly emerging as a vital conduit between urban and rural production and consumption systems. By promoting the rapid development of warehousing facilities and logistics hubs, policy initiatives have strengthened rural logistics capacity. Concurrently, efforts have been made to cultivate online agricultural brands and foster deeper integration with e-commerce platforms. These developments have laid the groundwork for a bidirectional circulation mechanism between urban and rural markets, thereby improving the downward distribution of industrial goods and the upward mobility of agricultural products. This integrative approach contributes to the seamless coordination of urban–rural supply chains and enhances efficiency in the production–consumption nexus across geographic divides (Zeng & Hu, 2024 ). 5.2.2 Analysis of the downward trend of digital village policy topics As can be seen from Fig. 7 , Topic 3 (Green Agriculture) and Topic 4 (Rural Reform) exhibited a downward trend from 2017 to 2024. (1)Topic 3 (Green Agriculture) has exhibited a general downward trend in thematic intensity. As a critical pillar of the broader green development agenda, green agriculture emphasizes the harmonious coexistence between agricultural production and ecological sustainability. Its core objectives include ensuring a stable supply of environmentally friendly agricultural products, enhancing farmers’ incomes, and safeguarding natural ecosystems (Koohafkan et al., 2012 ). Although green agriculture has received substantial policy attention and development support from the Chinese government—particularly within the broader framework of rural revitalization—its relative prominence in this study is limited. This is primarily because the focus of this research is on digital Village development, with an emphasis on digital agriculture rather than green agriculture. Consequently, the selected policy corpus includes relatively fewer documents that explicitly target green agricultural initiatives. (2)Topic 4 (Rural Reform) has exhibited a generally declining trend in thematic intensity, with intermittent fluctuations over time. China’s rural reform has progressed through distinct stages, beginning with the introduction of the household contract responsibility system and advancing toward comprehensive reforms in land tenure, property rights, rural governance structures, financial systems, and the establishment of rural social security mechanisms (Zuo et al., 2025 ).The construction of digital villages encompasses numerous areas within agriculture and rural development, necessitating deep and comprehensive reforms across multiple sectors. Consequently, rural reform emerged as a core policy focus during the initial stages of policy implementation. However, as the digital village initiatives progressed, attention to traditional reform topics gradually declined, shifting toward issues more closely associated with digital rural development. 5.2.3 digital village policy tend to be stable As can be seen from Fig. 8 ,Topic 2 (Agricultural Mechanization), Topic 5 (Rural Governance), Topic 6 (Industrial Finance), and Topic 9 (Targeted Poverty Alleviation) represent foundational pillars of China’s rural revitalization strategy and have consistently remained central to the national agricultural and rural development agenda. Over the years, the thematic intensities of these topics have exhibited no significant fluctuations, indicating a stable level of policy attention. This persistence reflects their sustained importance as long-term structural priorities in supporting rural transformation and ensuring the continuity of key functional domains within the digital countryside framework. 6 Conclusions and recommendations China’s digital village policy includes nine themes: e-commerce logistics, agricultural mechanization, green agriculture, rural reform, rural governance, industrial finance, digital village, digital commerce, and targeted poverty alleviation. These policies span a wide array of sectors and dimensions, reflecting a multidimensional framework characterized by multi-departmental collaboration, multi-objective orientation, and integrated implementation. Such a structure provides ample institutional support and development space for advancing rural revitalization, highlighting the comprehensiveness of the policy system.Among these themes, seven—namely agricultural mechanization, e-commerce logistics, digital countryside, and others—demonstrate stable or upward trends, while green agriculture and rural reform exhibit declining trajectories. This shift indicates a gradual transition of policy priorities from traditional drivers like agricultural mechanization toward newer domains such as digital commerce and logistics, underscoring the growing dominance of technology-driven innovation in guiding policy development.Although interest in themes related to “digital,” “smart,” and “intelligent” technologies has surged in recent years, the integration of cutting-edge technologies such as artificial intelligence and the Internet of Things into policy frameworks remains limited. This suggests that the depth and breadth of digital technology adoption in China’s digital village policies still require further enhancement. Drawing on the conclusions reached, this study outlines the following strategies for policy optimization. Firstly, enhance inter-agency coordination in policy issuance. Given the broad scope of digital village policies, it is essential to strengthen collaboration across departments. Currently, insufficient cross-departmental coordination remains a challenge. It is recommended to establish an interdepartmental coordination mechanism, promote joint policy issuance by multiple agencies, and build a new model of policy implementation that integrates government-enterprise cooperation and multi-stakeholder governance. This would improve both the coherence of policy formulation and the effectiveness of policy execution. Secondly, reinforce the thematic focus on digital technologies.To address weaknesses in digital agriculture, digital infrastructure, and digital governance, it is advised to expand the integration and application of cutting-edge technologies—such as artificial intelligence, blockchain, and the Internet of Things—into agricultural production, rural governance, and service systems. This would facilitate the convergence of diversified scenarios such as “Digital + Agriculture” and “Digital + Governance,” thereby advancing the deep empowerment of digital technologies. This study has the following limitations: First, due to the temporal constraints of policy issuance, some data may be missing. Second, certain overlaps among topic keywords were observed in the model outputs, and the naming of topics was influenced by subjective judgment. Future research could be extended to provincial-level policies or incorporate empirical evaluation of policy implementation outcomes. Declarations Conflict of interest The authors declare no conflict of interest. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Informed consent This article does not contain any studies with human participants performed by any of the authors. Funding Funding was provided by the Jiangxi Provincial Management Science Project “Enhancing the Strategic Support and Policy Advisory Capacity of Jiangxi Think Tanks” (Project No. 20244BAA10026), and the Key Project of the Jiangxi Provincial Social Science Planning Office “Tracking Study on Jiangxi as a Model for Rural Revitalization in the New Era” (Project No. 22SQ06). Author Contribution All authors contributed to the study conception and design. W and K drafted the initial version of the manuscript. C， W,L and T critically revised the manuscript for important intellectual content. All authors reviewed, edited, and approved the final version of the manuscript for submission. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6689009","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489172298,"identity":"7ba37179-bd08-492d-985f-d58dea834445","order_by":0,"name":"Cuicui Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYFACxgaGhAoJOX5m5sMPiNfy4IyFsWQ7W5oB8fY8bKtI3HCeR0GCKOX8M5IbPySwSTBuPszDYMBQYxNNUIvEjcRmiQQeCWazw7wHHjAcS8ttIKTFQCKxjSFBQoLN7DBfggFjw2FitRhI8Bg38xhIkKAFaI0BM7FaJM48BPrlgISBxGFgICcQ4xf+9vSHH3/+q6vv7z98+MGHGhvCWlBBAmnKR8EoGAWjYBTgAgANCTuHDv+IwwAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Cuicui","middleName":"","lastName":"Wang","suffix":""},{"id":489172299,"identity":"29fa21bf-dc07-4d92-885c-3b7be37d76f6","order_by":1,"name":"Kai HU","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"HU","suffix":""},{"id":489172300,"identity":"7a23a6ce-91e2-4da6-b4d5-44c7083aba79","order_by":2,"name":"Bo CAI","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"CAI","suffix":""},{"id":489172301,"identity":"eab2634e-9d8d-4a3a-8970-b2ce4b0d2a2d","order_by":3,"name":"Huiping Wu","email":"","orcid":"","institution":"Finance Department of Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Huiping","middleName":"","lastName":"Wu","suffix":""},{"id":489172302,"identity":"fbb36e89-3f82-4a2e-bb37-44d6e3a4b2b0","order_by":4,"name":"Wei Luo","email":"","orcid":"","institution":"Jiangxi Institute of Scientific and Technical Information","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Luo","suffix":""},{"id":489172303,"identity":"7fa27eaa-decb-4744-9581-a85a6d64e4a4","order_by":5,"name":"Ronghua Tang","email":"","orcid":"","institution":"Jiangxi Institute of Scientific and Technical Information","correspondingAuthor":false,"prefix":"","firstName":"Ronghua","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2025-05-17 21:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6689009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6689009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87500452,"identity":"1fec020b-8fb7-45b2-8192-ab9d949fbc8d","added_by":"auto","created_at":"2025-07-24 13:48:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50683,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual counts and proportional distribution of digital village policy documents (2017–2024)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6689009/v1/d651fce9d5f80852393eb0ac.png"},{"id":87500460,"identity":"da7b701c-d124-4ad7-8ef6-498ca69c31d1","added_by":"auto","created_at":"2025-07-24 13:48:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":222781,"visible":true,"origin":"","legend":"\u003cp\u003eWord Cloud Visualization\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6689009/v1/561d31a03ec219b83d2d34ad.png"},{"id":87501918,"identity":"699f0769-64e1-44c2-a31f-359c9e27377e","added_by":"auto","created_at":"2025-07-24 14:04:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29497,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the topic perplexity model\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6689009/v1/2807ed5984a6e1f1c023309c.png"},{"id":87500453,"identity":"405aab06-3d4b-41bf-812b-65726b69ad5b","added_by":"auto","created_at":"2025-07-24 13:48:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87307,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of pyLDAvis visualization results\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6689009/v1/02c10fa1e52c8390996e6624.png"},{"id":87502921,"identity":"e00cb00c-fa1c-4d6d-9539-9d40e9facae3","added_by":"auto","created_at":"2025-07-24 14:12:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104669,"visible":true,"origin":"","legend":"\u003cp\u003ePolicy Topic Heatmap\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6689009/v1/6bff4e35ef12ee126846b431.png"},{"id":87501916,"identity":"c2376c44-f769-4efa-a66c-41ec62fa1e15","added_by":"auto","created_at":"2025-07-24 14:04:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69839,"visible":true,"origin":"","legend":"\u003cp\u003eTopics showing an upward trend\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6689009/v1/63eaa0c65ddc6b4f817e2ea0.png"},{"id":87500465,"identity":"da44b1e8-7aa4-413f-95ed-e4f212654577","added_by":"auto","created_at":"2025-07-24 13:48:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":68603,"visible":true,"origin":"","legend":"\u003cp\u003eTopics showing a downward trend\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6689009/v1/f14ad61d825bc917ade698c8.png"},{"id":87501518,"identity":"1810dcf4-b025-47da-b08f-5ae2368f12ef","added_by":"auto","created_at":"2025-07-24 13:56:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":63573,"visible":true,"origin":"","legend":"\u003cp\u003eTopics showing stable trend\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6689009/v1/0690edf013872d87db726451.png"},{"id":88505132,"identity":"850c1e5b-5b5b-472a-b99a-c359208ebfb4","added_by":"auto","created_at":"2025-08-07 07:18:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1390940,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6689009/v1/7094551c-ae80-42f4-92a2-913974662780.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Topic evolution analysis in digital rural policies of China based on LDA model","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn the context of accelerating globalization and digitalization, digital technology has emerged as a pivotal force in driving socioeconomic development. It is progressively reshaping the operational landscape of multiple sectors and exerting a profound influence on urban\u0026ndash;rural development model. In 2017, China officially proposed leveraging information technologies to improve rural infrastructure and enhance the living standards of farmers. In 2019, the General Office of the CPC Central Committee and the State Council jointly issued the Strategic Plan for Digital Village Development, outlining a national vision for building digital villages (Wu \u0026amp; Zeng, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).The 20th National Congress of the Communist Party of China emphasized the importance of advancing national rejuvenation through Chinese-style modernization, promoting the development of a strong cyber power and Digital China, and accelerating rural revitalization and the construction of an agricultural powerhouse. These measures are part of China 's long-term development strategy (Zheng, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In 2025, China proposed the implementation of a Special Initiative for Strengthening, Benefiting, and Enriching Agriculture through Digital village Development.\u003c/p\u003e\u003cp\u003eIn recent years, China has introduced a series of policies to promote the construction of digital villages, providing strategic guidance for their development and effectively advancing the nationwide digital village initiative. Analyzing the thematic features and evolutionary trends of China's digital village policy over the years is of great significance for further optimizing the policy framework.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cp\u003eExisting research on digital village policy primarily focuses on four key areas:① The strategic significance of digital villages for rural revitalization. Zhao et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) argue that digital villages play a vital strategic role in addressing the \"three rural issues\" (agriculture, rural areas, and farmers) and strengthening rural governance systems, offering new pathways to overcome longstanding challenges in rural revitalization. Similarly, Snowball et al. (2021) suggest that the development of digital villages aligns with the broader digitalization era, injecting new vitality into rural development and unlocking \"digital dividends\" by effectively leveraging emerging technologies for value creation and acquisition.② Mechanisms through which digital villages promote rural revitalization. Liu et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) systematically explored the mechanisms by which digital villages facilitate rural revitalization from a theoretical perspective and validated\u0026mdash;through empirical analysis\u0026mdash;that digitalization of the rural economy and rural lifestyles are dual pathways empowering such revitalization. Building on a review of relevant literature, Rolandi et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) emphasized that the digital transformation of agriculture and rural areas will trigger profound changes across four dimensions: economy, environment, governance, and society.③ Evaluation and measurement of digital village policies. Ruiz et al. (2011) proposed the \"Omnia Mobilis\" hypothesis and introduced the PMC index model to assess digital village policies. Duan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) applied the entropy method and a coupling coordination model to quantitatively measure the coordination between central and local digital village policies and explored the effects of multi-level policy linkages. By drawing on grounded theory, Liu \u0026amp; Wei (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) constructed a policy tool selection framework for digital village construction, analyzing preferences and combinations in the use of policy instruments.④ Evaluation methods for policy texts. Liu et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) established a three-dimensional framework for policy analysis and adopted strategies such as social network analysis and the PMC index model to examine collaboration networks among actors, policy content, and implementation effectiveness during public health emergencies. Chen \u0026amp; Duan (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) utilized TF-IDF and LDA models to identify guarantees and policy objectives within implementation processes, constructing an implementation framework for open data policies. Cole et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) employed a difference-in-differences (DID) model combined with inverse probability of treatment weighting to compare the effects of primary healthcare policies on treated versus control groups of children. Lin et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted a text analysis of U.S. national public health and healthcare policy documents and cases released between 2000 and 2022.\u003c/p\u003e\u003cp\u003eThese prior studies provide a solid theoretical foundation for understanding the role and trajectory of digital village policies. However, few studies have systematically investigated the thematic evolution of such policies via policy text analysis. This paper applies text mining techniques, particularly the Latent Dirichlet Allocation (LDA) model, to uncover latent thematic structures within policy documents to fill this gap. It further investigates the evolution of digital village policy themes and proposes optimization strategies, offering a novel perspective for advancing the development of China's digital village policy system.\u003c/p\u003e"},{"header":"3 Selection and External Feature Analysis of Digital Village Policy Texts","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Selection of Digital Village Policy Texts\u003c/h2\u003e\u003cp\u003eTo ensure analytical consistency and institutional authority, this study focuses on digital village policy documents issued by the Central Committee of the Communist Party of China, the State Council, and key national ministries. Using keywords such as \u0026ldquo;digital village,\u0026rdquo; \u0026ldquo;digital governance,\u0026rdquo; \u0026ldquo;agricultural informatization,\u0026rdquo; \u0026ldquo;smart agriculture,\u0026rdquo; \u0026ldquo;Agricultural and Rural Modernization,\u0026rdquo; and \u0026ldquo;digital life,\u0026rdquo; a total of 88 relevant documents published between 2017 and 2024 were retrieved from official government portals and authoritative databases, including PKUlaw (Beida Fabao).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Descriptive Overview of the Digital Village Policy Corpus\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Classification of Policy Document Types\u003c/h2\u003e\u003cp\u003ePolicy documents are typically classified into five categories: regulations, notices, opinions, plans, and strategic directives (Li \u0026amp; Qin, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Quantitative analysis of the collected corpus (N\u0026thinsp;=\u0026thinsp;88) reveals a distinct typological distribution shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. : opinion documents dominate (44.32%), followed by plans (21.59%), with notices (17.05%) and strategic plans (15.91%) constituting smaller proportions. Regulatory documents are exceptionally rare, comprising merely 1.14% of the total.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTypological distribution of digital village policy documents\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProportion (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdvisory Opinions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDevelopmental Plans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdministrative Notices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrategic Directives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegulations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Temporal Distribution of Digital Village Policy Documents\u003c/h2\u003e\u003cp\u003eOverall, the issuance of digital village policy documents in China has exhibited a steadily increasing trend over the years, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, reflecting the central government's growing emphasis on constructing digital villages. Notably, a decline in the number of policy releases was observed in 2024, which may be attributed to delays in publication or incomplete collection of policy texts due to temporal lags in official disclosure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Institutional Analysis of Digital Village Policy Issuing Authorities\u003c/h2\u003e\u003cp\u003eFrom the perspective of issuing authorities, 43 policy documents (48.86%) were issued by a single agency, 24 documents (27.27%) were jointly issued by two agencies, and 21 documents (23.87%) were co-issued by three or more departments.\u003c/p\u003e\u003cp\u003eGephi-based network analysis reveals the following characteristics of the issuing authorities: First, the highest frequency of joint issuance (16 instances) occurred under the names of the Central Committee of the Communist Party of China and the State Council, indicating a high level of authority and strong central government emphasis. Second, policy issuance involved a broad range of institutions\u0026mdash;35 ministries and commissions in total\u0026mdash;participating in 232 instances of joint issuance, suggesting a high degree of interdepartmental collaboration and a proactive response to central directives.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Thematic Evolution of Digital Village Policies Based on the LDA Model","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e4.1\u003c/b\u003e.\u003cb\u003e1 LDA topic model\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eLDA, first proposed by Blei et al. in 2003 (Blei et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), is a three-layer Bayesian probabilistic model. Its core principle is that each document comprises a mixture of topics, and a distribution of words characterizes each topic. By leveraging unsupervised learning techniques, LDA enables the extraction of latent thematic structures from large-scale textual data, thereby uncovering hidden topic patterns within extensive document collections or corpora (Momtazi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e;Martinez \u0026amp; Kak, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Policy documents are typically highly specialized and are often presented in unstructured formats (Wu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a widely adopted text mining technique in recent years, the LDA topic model has proven effective in identifying underlying themes in policy texts and enhancing their interpretability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Data Pre-processing Methods\u003c/h2\u003e\u003cp\u003eThis study utilized the Jieba library in Python to perform word segmentation. Data Pre-processing was conducted by incorporating a user-defined lexicon, a stopword list, and a synonym dictionary. The user-defined lexicon primarily included domain-specific proper nouns closely related to digital village development, such as \u0026ldquo;smart agriculture\u0026rdquo; and \u0026ldquo;digital governance.\u0026rdquo; The synonym dictionary was used to normalize semantically equivalent terms with varying expressions, such as \u0026ldquo;rural e-commerce\u0026rdquo; and \u0026ldquo;rural electronic commerce.\u0026rdquo; Stopwords\u0026mdash;function words with limited semantic value (e.g., prepositions)\u0026mdash;were removed using the stop word list of Harbin Institute of Technology. Subsequently, word frequency statistics were generated, and a word cloud was constructed to visualize the most salient terms, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides an intuitive visualization of the keywords extracted from the policy documents and their relative importance. It exhibits the following characteristics:First, high-frequency terms such as \u0026ldquo;construction,\u0026rdquo; \u0026ldquo;development,\u0026rdquo; \u0026ldquo;services,\u0026rdquo; \u0026ldquo;agriculture,\u0026rdquo; \u0026ldquo;rural,\u0026rdquo; and \u0026ldquo;agricultural products\u0026rdquo; highlight the primary areas of focus in digital rural policy.Second, keywords like \u0026ldquo;technology,\u0026rdquo; \u0026ldquo;digital,\u0026rdquo; and \u0026ldquo;innovation\u0026rdquo; maintain substantial weight, indicating the prominent role of digital technologies in policy design.Third, terms such as \u0026ldquo;organization,\u0026rdquo; \u0026ldquo;management,\u0026rdquo; and \u0026ldquo;improvement\u0026rdquo; suggest that the policies emphasize not only technical and infrastructural development but also the enhancement of institutional systems in rural governance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Optimal Number of Topics Determination\u003c/h2\u003e\u003cp\u003eTopic modeling was conducted using the LDA algorithm implemented via the Gensim library in Python. The model was trained with default values for the hyperparameters α and β, and the number of iterations was set to 500. The methods for determining the optimal number of topics include the perplexity model, the topic coherence model, and visual inspection using pyLDAvis (Feng et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study adopts the perplexity model and supplements it with pyLDAvis-based visualization of clustering results to identify the optimal number of topics.\u003c/p\u003e\u003cp\u003eAs illustrated by the perplexity curve in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the model achieves its lowest perplexity when the number of topics reaches nine, aligning with the \"elbow\" method commonly used to identify the optimal point (Zhang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This indicates that the model's explanatory power is maximized at this topic count. Furthermore, the pyLDAvis visualization in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates that when the number of topics is set to nine, the topic clusters are evenly distributed, with no overly dominant or insignificant bubbles, suggesting a well-balanced and relatively independent topic structure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Theme Extraction of Policy Text\u003c/h2\u003e\u003cp\u003eThe high-probability term distributions for China\u0026rsquo;s digital village policy themes, derived through the LDA theme model,, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A total of nine topics were extracted, with ten representative keywords listed for each topic. Based on the probabilistic distribution of characteristic terms for each topic (Yl\u0026auml;-Anttila et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the nine themes were sequentially labeled as follows: Topic 1 (E-commerce Logistics), Topic 2 (Agricultural Mechanization), Topic 3 (Green Agriculture), Topic 4 (Rural Reform), Topic 5 (Rural Governance), Topic 6 (Industrial Finance), Topic 7 (Digital Countryside), Topic 8 (Digital Commerce), and Topic 9 (Targeted Poverty Alleviation).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTheme-word distribution\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubject No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThematic Overview\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTop 10 High Probability Feature Words\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE-commerce Logistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012* Urban-Rural\u0026thinsp;+\u0026thinsp;0.008* Countryside\u0026thinsp;+\u0026thinsp;0.007* Logistics\u0026thinsp;+\u0026thinsp;0.007* Judiciary\u0026thinsp;+\u0026thinsp;0.007* Rural E-commerce\u0026thinsp;+\u0026thinsp;0.007* Lawfully\u003c/p\u003e\u003cp\u003e+\u0026thinsp;0.006* Protection\u0026thinsp;+\u0026thinsp;0.006* Commerce\u0026thinsp;+\u0026thinsp;0.005* Live streaming\u0026thinsp;+\u0026thinsp;0.004* Governance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural Machinery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.040* Mechanization\u0026thinsp;+\u0026thinsp;0.038* Agricultural Machinery + 0.021* Primary Processing\u0026thinsp;+\u0026thinsp;0.007* Technical Equipment\u0026thinsp;+\u0026thinsp;0.006* Breeding\u0026thinsp;+\u0026thinsp;0.006* R\u0026amp;D\u0026thinsp;+\u0026thinsp;0.006* Informatization\u0026thinsp;+\u0026thinsp;0.006* Efficiency\u0026thinsp;+\u0026thinsp;0.005* Training\u0026thinsp;+\u0026thinsp;0.005* Grading\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGreen Agriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018* Green\u0026thinsp;+\u0026thinsp;0.009* Base\u0026thinsp;+\u0026thinsp;0.008* Efficiency\u0026thinsp;+\u0026thinsp;0.007* Farm\u0026thinsp;+\u0026thinsp;0.006* Lead\u0026thinsp;+\u0026thinsp;0.006* Breeding\u0026thinsp;+\u0026thinsp;0.006* Agricultural prosperity\u0026thinsp;+\u0026thinsp;0.006* Livestock and Poultry\u0026thinsp;+\u0026thinsp;0.005* Cooperative\u0026thinsp;+\u0026thinsp;0.004* R\u0026amp;D\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural Reform\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019* Countryside\u0026thinsp;+\u0026thinsp;0.012* Rural Revitalization\u0026thinsp;+\u0026thinsp;0.007* Protection\u0026thinsp;+\u0026thinsp;0.007* Reform\u0026thinsp;+\u0026thinsp;0.006* Entrepreneurship\u0026thinsp;+\u0026thinsp;0.005* Culture\u0026thinsp;+\u0026thinsp;0.005* Poverty Alleviation\u0026thinsp;+\u0026thinsp;0.005* Ecology\u0026thinsp;+\u0026thinsp;0.005* Cultivated Land\u0026thinsp;+\u0026thinsp;0.004* Urban-Rural\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural Governance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007* Farmers\u0026thinsp;+\u0026thinsp;0.007* Protection\u0026thinsp;+\u0026thinsp;0.007* Governance\u0026thinsp;+\u0026thinsp;0.006* Reform\u0026thinsp;+\u0026thinsp;0.006* Deepening\u0026thinsp;+\u0026thinsp;0.006* Culture\u0026thinsp;+\u0026thinsp;0.005* Strategy\u0026thinsp;+\u0026thinsp;0.005* Ecology\u0026thinsp;+\u0026thinsp;0.005* Region\u0026thinsp;+\u0026thinsp;0.004* Consumption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustrial Finance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011* Breeding\u0026thinsp;+\u0026thinsp;0.008* Transformation\u0026thinsp;+\u0026thinsp;0.007* Finance\u0026thinsp;+\u0026thinsp;0.007* Planting\u0026thinsp;+\u0026thinsp;0.006* Financing\u0026thinsp;+\u0026thinsp;0.006* Supporting Facilities\u0026thinsp;+\u0026thinsp;0.005* Grain\u0026thinsp;+\u0026thinsp;0.005* Logistics\u0026thinsp;+\u0026thinsp;0.005* Production Area\u0026thinsp;+\u0026thinsp;0.005* Planning\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Village\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017* Data\u0026thinsp;+\u0026thinsp;0.016* Countryside\u0026thinsp;+\u0026thinsp;0.014* Digitalization\u0026thinsp;+\u0026thinsp;0.013* Digital\u0026thinsp;+\u0026thinsp;0.013* Informatization\u0026thinsp;+\u0026thinsp;0.011* Digital Village\u0026thinsp;+\u0026thinsp;0.009* Smart\u0026thinsp;+\u0026thinsp;0.008* Intelligent\u0026thinsp;+\u0026thinsp;0.007* Internet\u0026thinsp;+\u0026thinsp;0.006* System\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital Commerce\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.035* Digital\u0026thinsp;+\u0026thinsp;0.027* Data\u0026thinsp;+\u0026thinsp;0.021* 5G\u0026thinsp;+\u0026thinsp;0.016* Digitalization\u0026thinsp;+\u0026thinsp;0.014* E-commerce\u0026thinsp;+\u0026thinsp;0.009* Intelligent\u0026thinsp;+\u0026thinsp;0.008* Smart\u0026thinsp;+\u0026thinsp;0.007* Governance\u0026thinsp;+\u0026thinsp;0.006* Meteorology\u0026thinsp;+\u0026thinsp;0.006* Digital Transformation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTargeted Poverty Alleviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030* Rural Revitalization\u0026thinsp;+\u0026thinsp;0.021* Poverty Alleviation\u0026thinsp;+\u0026thinsp;0.014* Countryside\u0026thinsp;+\u0026thinsp;0.009* Tackling Key Problems\u0026thinsp;+\u0026thinsp;0.008* Assistance\u0026thinsp;+\u0026thinsp;0.008* Rural Governance\u0026thinsp;+\u0026thinsp;0.006* Township\u0026thinsp;+\u0026thinsp;0.006* Consolidation\u0026thinsp;+\u0026thinsp;0.006* Party Committee\u0026thinsp;+\u0026thinsp;0.005* Achievements\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Thematic Analysis of Digital Village Policies","content":"\u003cp\u003eThis section systematically analyses policy texts from two dimensions\u0026mdash;topic intensity and thematic evolution\u0026mdash;to uncover shifting policy priorities across different stages of digital village development in China. The findings are intended to provide theoretical support for optimising the national digital village policy framework.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Topic Intensity Analysis\u003c/h2\u003e\u003cp\u003eTo examine the evolutionary trajectories of thematic emphases, this study employs a post-discretization method (Shan \u0026amp; Li, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to quantify annual topic intensities based on the output of the LDA model. A topic heatmap was constructed accordingly, where color saturation represents the relative strength of each topic over time, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThematically, digital Village policies have predominantly centered on areas such as rural reform (Topic 4), digital village (Topic 7), green agriculture (Topic 3), and digital commerce (Topic 8). Temporally, there is a discernible shift in policy focus from rural reform (Topic 4) to digital village (Topic 7), suggesting a strategic transition from broad structural reform to more targeted and technologically oriented digital infrastructure development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Topic Evolution Trend Analysis\u003c/h2\u003e\u003cp\u003eAs China\u0026rsquo;s Digital Village Strategy advances, policy priorities have undergone phased adjustments. Systematic analysis of these evolutionary patterns is instrumental in capturing broader policy dynamics and informing future optimization. Based on temporal variations in topic intensity, the nine identified themes are categorized into three groups: ascent, descent, and stability.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e5.2.1 Analysis of the rising trend of digital village policy topics\u003c/h2\u003e\u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Topic 8 (Digital Commerce), Topic 7 (Digital Village), and Topic 1 (E-commerce Logistics) exhibited a sustained upward trajectory between 2017 and 2024.\u003c/p\u003e\u003cp\u003e(1)Topic 8 (Digital Commerce) has exhibited a consistently rising trend in thematic intensity since 2019, underscoring its increasing policy relevance within China\u0026rsquo;s digital village development agenda. The rapid proliferation of rural e-commerce platforms has effectively shortened the supply chain between agricultural producers and end consumers, thereby lowering transaction and distribution costs across the agricultural value chain. By leveraging digital platforms, consumers gain access to geographically dispersed, high-quality agricultural products, thus helping to resolve the dual bottlenecks of \u0026ldquo;sales difficulty\u0026rdquo; on the supply side and \u0026ldquo; purchase difficulty\u0026rdquo; on the demand side. These dynamics have not only enhanced rural household incomes but also improved farmers\u0026rsquo; digital literacy and technology adoption capacity, further accelerating the construction of the digital countryside (Han \u0026amp; Gong, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Amid the ongoing expansion of the digital economy, numerous rural regions have mobilized endogenous resources and cultivated localized specialty industries through e-commerce platforms. This has led to the emergence of digitally enabled commercial clusters such as \u0026ldquo;Taobao Villages\u0026rdquo; and \u0026ldquo;Taobao Towns,\u0026rdquo; which have become focal nodes of rural digital commerce. These developments demonstrate the dual economic and social dividends generated by rural e-commerce and affirm its strategic role as a core pillar in China\u0026rsquo;s broader digital countryside initiative.\u003c/p\u003e\u003cp\u003e(2)Topic 7 (Digital Village) ranks among the most salient themes, exhibiting a generally upward but fluctuating trajectory in intensity, with a marked increase observed in 2024. The construction of digital countryside has emerged as a critical policy initiative, formulated in response to the structural realities of rural development in China (Gu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It not only provides robust support for the implementation of the rural revitalization strategy but also increasingly serves as a strategic conduit linking rural development goals with the broader national agenda of \u0026ldquo;Digital China.\u0026rdquo; Enabled by modern information technologies, digital countryside initiatives are catalyzing transformative changes across multiple dimensions\u0026mdash;including agricultural production, rural livelihoods, and governance systems (Yan, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e(3)Topic 1 (E-commerce Logistics) has exhibited a gradual upward trend in thematic intensity over the examined period. As a pivotal component of digital commerce, e-commerce logistics has benefited significantly from digital technology, contributing to the steady enhancement of rural e-commerce infrastructure, logistics networks, and the digitization of agricultural product distribution. Anchored in the principle of shared logistics resources, rural e-commerce logistics is increasingly emerging as a vital conduit between urban and rural production and consumption systems. By promoting the rapid development of warehousing facilities and logistics hubs, policy initiatives have strengthened rural logistics capacity. Concurrently, efforts have been made to cultivate online agricultural brands and foster deeper integration with e-commerce platforms. These developments have laid the groundwork for a bidirectional circulation mechanism between urban and rural markets, thereby improving the downward distribution of industrial goods and the upward mobility of agricultural products. This integrative approach contributes to the seamless coordination of urban\u0026ndash;rural supply chains and enhances efficiency in the production\u0026ndash;consumption nexus across geographic divides (Zeng \u0026amp; Hu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e5.2.2 Analysis of the downward trend of digital village policy topics\u003c/h2\u003e\u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Topic 3 (Green Agriculture) and Topic 4 (Rural Reform) exhibited a downward trend from 2017 to 2024.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(1)Topic 3 (Green Agriculture) has exhibited a general downward trend in thematic intensity. As a critical pillar of the broader green development agenda, green agriculture emphasizes the harmonious coexistence between agricultural production and ecological sustainability. Its core objectives include ensuring a stable supply of environmentally friendly agricultural products, enhancing farmers\u0026rsquo; incomes, and safeguarding natural ecosystems (Koohafkan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although green agriculture has received substantial policy attention and development support from the Chinese government\u0026mdash;particularly within the broader framework of rural revitalization\u0026mdash;its relative prominence in this study is limited. This is primarily because the focus of this research is on digital Village development, with an emphasis on digital agriculture rather than green agriculture. Consequently, the selected policy corpus includes relatively fewer documents that explicitly target green agricultural initiatives.\u003c/p\u003e\u003cp\u003e(2)Topic 4 (Rural Reform) has exhibited a generally declining trend in thematic intensity, with intermittent fluctuations over time. China\u0026rsquo;s rural reform has progressed through distinct stages, beginning with the introduction of the household contract responsibility system and advancing toward comprehensive reforms in land tenure, property rights, rural governance structures, financial systems, and the establishment of rural social security mechanisms (Zuo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).The construction of digital villages encompasses numerous areas within agriculture and rural development, necessitating deep and comprehensive reforms across multiple sectors. Consequently, rural reform emerged as a core policy focus during the initial stages of policy implementation. However, as the digital village initiatives progressed, attention to traditional reform topics gradually declined, shifting toward issues more closely associated with digital rural development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e5.2.3 digital village policy tend to be stable\u003c/h2\u003e\u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e,Topic 2 (Agricultural Mechanization), Topic 5 (Rural Governance), Topic 6 (Industrial Finance), and Topic 9 (Targeted Poverty Alleviation) represent foundational pillars of China\u0026rsquo;s rural revitalization strategy and have consistently remained central to the national agricultural and rural development agenda. Over the years, the thematic intensities of these topics have exhibited no significant fluctuations, indicating a stable level of policy attention. This persistence reflects their sustained importance as long-term structural priorities in supporting rural transformation and ensuring the continuity of key functional domains within the digital countryside framework.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"6 Conclusions and recommendations","content":"\u003cp\u003eChina\u0026rsquo;s digital village policy includes nine themes: e-commerce logistics, agricultural mechanization, green agriculture, rural reform, rural governance, industrial finance, digital village, digital commerce, and targeted poverty alleviation. These policies span a wide array of sectors and dimensions, reflecting a multidimensional framework characterized by multi-departmental collaboration, multi-objective orientation, and integrated implementation. Such a structure provides ample institutional support and development space for advancing rural revitalization, highlighting the comprehensiveness of the policy system.Among these themes, seven\u0026mdash;namely agricultural mechanization, e-commerce logistics, digital countryside, and others\u0026mdash;demonstrate stable or upward trends, while green agriculture and rural reform exhibit declining trajectories. This shift indicates a gradual transition of policy priorities from traditional drivers like agricultural mechanization toward newer domains such as digital commerce and logistics, underscoring the growing dominance of technology-driven innovation in guiding policy development.Although interest in themes related to \u0026ldquo;digital,\u0026rdquo; \u0026ldquo;smart,\u0026rdquo; and \u0026ldquo;intelligent\u0026rdquo; technologies has surged in recent years, the integration of cutting-edge technologies such as artificial intelligence and the Internet of Things into policy frameworks remains limited. This suggests that the depth and breadth of digital technology adoption in China\u0026rsquo;s digital village policies still require further enhancement.\u003c/p\u003e\u003cp\u003eDrawing on the conclusions reached, this study outlines the following strategies for policy optimization.\u003c/p\u003e\u003cp\u003eFirstly, enhance inter-agency coordination in policy issuance. Given the broad scope of digital village policies, it is essential to strengthen collaboration across departments. Currently, insufficient cross-departmental coordination remains a challenge. It is recommended to establish an interdepartmental coordination mechanism, promote joint policy issuance by multiple agencies, and build a new model of policy implementation that integrates government-enterprise cooperation and multi-stakeholder governance. This would improve both the coherence of policy formulation and the effectiveness of policy execution.\u003c/p\u003e\u003cp\u003eSecondly, reinforce the thematic focus on digital technologies.To address weaknesses in digital agriculture, digital infrastructure, and digital governance, it is advised to expand the integration and application of cutting-edge technologies\u0026mdash;such as artificial intelligence, blockchain, and the Internet of Things\u0026mdash;into agricultural production, rural governance, and service systems. This would facilitate the convergence of diversified scenarios such as \u0026ldquo;Digital\u0026thinsp;+\u0026thinsp;Agriculture\u0026rdquo; and \u0026ldquo;Digital\u0026thinsp;+\u0026thinsp;Governance,\u0026rdquo; thereby advancing the deep empowerment of digital technologies.\u003c/p\u003e\u003cp\u003eThis study has the following limitations: First, due to the temporal constraints of policy issuance, some data may be missing. Second, certain overlaps among topic keywords were observed in the model outputs, and the naming of topics was influenced by subjective judgment.\u003c/p\u003e\u003cp\u003eFuture research could be extended to provincial-level policies or incorporate empirical evaluation of policy implementation outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cb\u003eConflict of interest\u003c/b\u003e\u003c/strong\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eFunding was provided by the Jiangxi Provincial Management Science Project \u0026ldquo;Enhancing the Strategic Support and Policy Advisory Capacity of Jiangxi Think Tanks\u0026rdquo; (Project No. 20244BAA10026), and the Key Project of the Jiangxi Provincial Social Science Planning Office \u0026ldquo;Tracking Study on Jiangxi as a Model for Rural Revitalization in the New Era\u0026rdquo; (Project No. 22SQ06).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. W and K drafted the initial version of the manuscript. C， W,L and T critically revised the manuscript for important intellectual content. All authors reviewed, edited, and approved the final version of the manuscript for submission.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eAll material data and models that support the results of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBlei, D. M., Ng, A. Y., \u0026amp; Jordan, M. I. (2003). Latent Dirichlet allocation. \u003cem\u003eJournal of Machine Learning Research, 3\u003c/em\u003e(Jan), 993\u0026ndash;1022.\u003c/li\u003e\n\u003cli\u003eChen, L., \u0026amp; Duan, Y. Q. (2020). 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Promoting agricultural and rural modernization by comprehensively deepening rural reform: Theoretical logic, historical experience and practical path. \u003cem\u003eJournal of Nanjing Agricultural University (Social Sciences Edition), 25\u003c/em\u003e(1), 1\u0026ndash;14. https://doi.org/10.19714/j.cnki.1671-7465.2025.0001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Digital village, LDA model, Theme evolution, Theme mining","lastPublishedDoi":"10.21203/rs.3.rs-6689009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6689009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the continuous advancement of information technology, digital innovation has emerged as a pivotal driver of rural revitalization and agricultural modernization in China. This study analyzes 88 digital rural policy documents issued at the central government level between 2017 and 2024. Using the Latent Dirichlet Allocation (LDA) topic modeling approach, we identify nine key thematic areas: agricultural mechanization, e-commerce logistics, digital villages, digital commerce, rural governance, industrial finance, poverty alleviation, green agriculture, and rural reform, further analysis of its evolution trend found that: First, the policy topics exhibit broad coverage, spanning diverse dimensions such as agricultural production, e-commerce logistics, and rural governance. Second, thematic differentiation is evident: seven themes\u0026mdash;including agricultural mechanization, e-commerce logistics, and digital villages\u0026mdash;demonstrate sustained growth or stability, whereas green agriculture and rural reform show a declining trend. Third, the thematic focus of China\u0026rsquo;s digital rural policies has evolved from traditional components such as mechanization toward digitally driven transformation, exemplified by the rise of smart agriculture. Based on these insights, we propose targeted recommendations to enhance inter-agency coordination in policy formulation and reinforce policy priorities' alignment with core digital technologies.\u003c/p\u003e","manuscriptTitle":"Topic evolution analysis in digital rural policies of China based on LDA model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 13:48:12","doi":"10.21203/rs.3.rs-6689009/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-06T17:15:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T02:08:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146738305007985658242470528914678855567","date":"2025-09-15T14:33:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T09:54:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2845041618020569919131281070482533838","date":"2025-08-02T08:42:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-22T12:14:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-22T12:06:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T18:01:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-25T17:30:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-06-25T17:27:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"860189fd-7a82-49c6-ac12-70f06b07bdc3","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51932295,"name":"Social science/Economics"},{"id":51932296,"name":"Social science/Social policy"}],"tags":[],"updatedAt":"2026-01-02T09:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-24 13:48:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6689009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6689009","identity":"rs-6689009","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}