{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T23:10:40Z","timestamp":1790118640831,"version":"4.0.1"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T00:00:00Z","timestamp":1627862400000},"content-version":"vor","delay-in-days":213,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2015ZX18"],"award-info":[{"award-number":["2015ZX18"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>\n                    A digital campus will generate a large amount of student\u2010related data. How to analyze and apply these data has become the key to improving the management level of students. The analysis of student behavior data can not only assist schools in early warning of dangerous events and strengthen school safety but also can use real data to describe student behavior, thereby providing quantitative data support for scholarship and grant evaluation. This paper takes a university student as the research object, collects various data in the digital campus platform, and uses an adaptive\n                    <jats:italic>K<\/jats:italic>\n                    \u2010means algorithm in the machine learning algorithm to cluster the data. Analyze the behavior of college students from the clustering results, so as to provide a basis for the education management and learning ability improvement of college students. Specifically, the student\u2019s study, life, and consumption data are selected as the data to describe the student\u2019s behavior at school. This data is input into the adaptive\n                    <jats:italic>K<\/jats:italic>\n                    \u2010means algorithm to obtain different types of student consumption habits, living habits, and learning habits. Through the analysis results, it can be found that the problem of the group of students with low financial ability, the problem of too long online time for students, and the number of books borrowed are too low. According to the characteristics of these problems, teachers and schools are provided with targeted management suggestions. The analysis of student behavior based on machine learning technology provides a reference for the formulation of students\u2019 school management policies and provides teachers with information on students\u2019 personality characteristics, which is conducive to improving teachers\u2019 teaching effects. In short, the management of the results of student behavior analysis can provide a basis for the school to formulate reasonable management policies, thereby promoting precision management and scientific decision\u2010making.\n                  <\/jats:p>","DOI":"10.1155\/2021\/3126347","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T15:50:27Z","timestamp":1627919427000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A College Student Behavior Analysis and Management Method Based on Machine Learning Technology"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-9298","authenticated-orcid":false,"given":"Xiaoying","family":"Shen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7963-9525","authenticated-orcid":false,"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,2]]},"reference":[{"key":"e_1_2_11_1_2","article-title":"Orderness predicts academic performance: behavioral analysis on campus lifestyle","volume":"15","author":"Cao Y.","year":"2017","journal-title":"Journal of the Royal Society Interface"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-017-9371-y"},{"key":"e_1_2_11_3_2","doi-asserted-by":"crossref","unstructured":"ZhouM. 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