Reconstruction and interpretable analysis of international digital trade development level measurement based on machine learning and SHAP algorithm

Reconstruction and interpretable analysis of international digital trade development level measurement based on machine learning and SHAP algorithm | 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 Reconstruction and interpretable analysis of international digital trade development level measurement based on machine learning and SHAP algorithm Jingyi LAO, Liurong PAN, Wei LI, Weiye LIANG, Jiali XIE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9303847/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The purpose of this paper is to evaluate the performance of machine learning model and put forward an interpretable prediction framework for the development of international digital trade. Based on the digital trade data of 55 countries from 2010 to 2023, the evaluation index system of international digital trade development level is constructed from seven dimensions, including digital trade potential. Through XGBoost and SHAP machine learning models, the importance of different variables to the development level of international digital trade is identified. The results show that: 1) from 2010 to 2023, the development trend of international digital trade is relatively fast, and the overall score has increased from 0.079 in 2010 to 0.122 in 2023, with an increase of 54.4%; 2) From the comprehensive score of each dimension, the score of digital trade infrastructure is always ahead and the overall score shows a steady and slight upward trend, and the score of digital trade barriers is low, which is the weak link in the development of digital trade. Based on this, in order to improve the development efficiency of international digital trade, this paper puts forward five suggestions, including strengthening the cultivation and transformation of effective patents. Physical sciences/Engineering Physical sciences/Mathematics and computing development level of international digital trade Machine learning XGBoost algorithm SHAP value analysis Full Text Additional Declarations No competing interests reported. Supplementary Files DATA.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Apr, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor invited by journal 07 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 2026 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-9303847","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":621374114,"identity":"e67bf2fd-b231-46fa-b628-0908dcdfb73d","order_by":0,"name":"Jingyi LAO","email":"","orcid":"","institution":"Beibu Gulf University","correspondingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"LAO","suffix":""},{"id":621374115,"identity":"2c243404-2bfb-4255-9521-4003e6d44c47","order_by":1,"name":"Liurong 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international digital trade, Machine learning, XGBoost algorithm, SHAP value analysis","lastPublishedDoi":"10.21203/rs.3.rs-9303847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9303847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe purpose of this paper is to evaluate the performance of machine learning model and put forward an interpretable prediction framework for the development of international digital trade. Based on the digital trade data of 55 countries from 2010 to 2023, the evaluation index system of international digital trade development level is constructed from seven dimensions, including digital trade potential. Through XGBoost and SHAP machine learning models, the importance of different variables to the development level of international digital trade is identified. The results show that: 1) from 2010 to 2023, the development trend of international digital trade is relatively fast, and the overall score has increased from 0.079 in 2010 to 0.122 in 2023, with an increase of 54.4%; 2) From the comprehensive score of each dimension, the score of digital trade infrastructure is always ahead and the overall score shows a steady and slight upward trend, and the score of digital trade barriers is low, which is the weak link in the development of digital trade. Based on this, in order to improve the development efficiency of international digital trade, this paper puts forward five suggestions, including strengthening the cultivation and transformation of effective patents.\u003c/p\u003e","manuscriptTitle":"Reconstruction and interpretable analysis of international digital trade development level measurement based on machine learning and SHAP algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 03:14:44","doi":"10.21203/rs.3.rs-9303847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-30T08:54:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T00:55:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70366437267237954143459212267352363989","date":"2026-04-30T00:48:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170848598112741525758787945718848932716","date":"2026-04-11T10:12:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T14:05:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T13:21:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-03T03:53:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T03:53:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-02T13:24:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ad3d18c-302f-4dc8-9b4f-1f24abe8d666","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-30T08:54:38+00:00","index":38,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T00:55:59+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"70366437267237954143459212267352363989","date":"2026-04-30T00:48:51+00:00","index":35,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66129227,"name":"Physical sciences/Engineering"},{"id":66129228,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-16T03:14:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 03:14:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9303847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9303847","identity":"rs-9303847","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}