{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:18:44Z","timestamp":1771003124827,"version":"3.50.1"},"reference-count":14,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>With the advancement and widespread adoption of cloud computing technology, its application in the educational sector has deepened, showcasing unique advantages in the sharing of art education resources particularly. The essence of Chinese traditional culture, calligraphy education, is in urgent need of innovation and dissemination through modern technological means. However, existing research on the application of cloud computing in art education, especially in the sharing of calligraphy education resources, remains insufficient, with a lack of targeted demand modeling and resource-sharing mechanism studies. This study explores user needs on art education resource-sharing platforms through regression analysis, constructing demand modeling to provide a scientific, data-driven resource allocation scheme. Furthermore, in response to the specific requirements for sharing art education resources under a cloud computing environment, this research employs matching theory to propose a sharing mechanism aimed at optimizing resource allocation and enhancing efficiency. Through these investigations, not only is the application research of cloud computing in the field of art education enriched, but also theoretical support and implementation strategies for the practice of art education resource sharing are also provided. Research on the art education resource sharing mechanism based on matching theory has led to the development of a modeling method that precisely captures user needs and behavioral patterns. The model has been optimized to enhance its applicability in scenarios involving art education. Empirical results demonstrate the superiority of this model in several areas, namely, the satisfaction in resource sharing, the resource sharing rate, and the social utility. Notably, the model also exhibits significant advantages in computational efficiency. These findings provide a scientific basis and practical tools for resource sharing in the realm of art education.<\/jats:p>","DOI":"10.1177\/14727978251322292","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T02:26:24Z","timestamp":1741746384000},"page":"779-793","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimizing art education resource sharing through cloud computing: A study on demand modeling and matching theory"],"prefix":"10.1177","volume":"25","author":[{"given":"Jungang","family":"Ding","sequence":"first","affiliation":[{"name":"Faculty of Teacher Education and Humanities, University of Baguio"}]}],"member":"179","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.02.024"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.18280\/isi.290207"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.22937\/IJCSNS.2022.22.4.52"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJCEELL.2019.102771"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8202229"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6325329"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13677-023-00455-1"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1177\/0020294020948088"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7941"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2172817"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-018-0195-8"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.11.414"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/4818056"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19122817"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251322292","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978251322292","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251322292","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:31:29Z","timestamp":1771000289000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978251322292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":14,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1177\/14727978251322292"],"URL":"https:\/\/doi.org\/10.1177\/14727978251322292","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}