Research on Intelligent Perception Evaluation of Spatial Quality in Historic Cultural Blocks Based on Machine Learning

Research on Intelligent Perception Evaluation of Spatial Quality in Historic Cultural Blocks Based on Machine Learning | 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 Research on Intelligent Perception Evaluation of Spatial Quality in Historic Cultural Blocks Based on Machine Learning Yuan Zhang, Hang Hang, Guohua Ji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7534581/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 As an important carrier of urban cultural heritage, the spatial quality of historic cultural blocks directly affects public experience and the effectiveness of cultural inheritance. However, traditional evaluation methods mostly rely on manual surveys and subjective judgments, suffering from defects such as low efficiency, limited coverage, and single-dimensionality. Existing quantitative studies still have deficiencies in multi-source data fusion, dynamic perception, and intelligent evaluation. To address this, this study proposes a comprehensive evaluation method that integrates multi-source spatial data, subjective perception scores, and machine learning. Objective and subjective evaluation index data are obtained quantitatively through image semantic segmentation, GIS spatial analysis, space syntax, questionnaire surveys, and the ELO rating system. In the model construction stage, various classic regression algorithms are applied. After training, evaluation, and optimization of the models, the XGBoost algorithm is finally selected as the intelligent perception evaluation model for spatial quality. Taking Suzhou Taohuawu Block as an example, a closed-loop process of "objective quantification - subjective prediction and visualization - comparative analysis - strategy feedback" is formed. This research aims to break through the limitations of traditional evaluation, provide data-driven decision support for the renewal and protection of blocks, and promote the application of machine learning in cultural heritage protection. Humanities/Cultural and media studies Social science/Cultural and media studies Physical sciences/Mathematics and computing Multi-source data Machine learning Historic cultural blocks Spatial quality Intelligent perception evaluation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Dec, 2025 Reviews received at journal 18 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 02 Nov, 2025 Editor invited by journal 21 Oct, 2025 Editor assigned by journal 17 Oct, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7534581","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542646851,"identity":"fb23e5f6-46c1-4eb4-b7f5-4c97be3f46de","order_by":0,"name":"Yuan Zhang","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Zhang","suffix":""},{"id":542646852,"identity":"1065c50a-3d8e-4859-b12a-3182a77ea242","order_by":1,"name":"Hang Hang","email":"","orcid":"","institution":"Huangpu District Urban Renewal Promotion 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