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Traditional collaborative filtering methods have achieved some success in recommender systems. However, with the increase of data scale and the complexity of entrepreneurial projects, the effect of traditional methods is gradually limited. This study uses a data set containing a large number of entrepreneurial project data and compares the collaborative filtering model based on deep learning with traditional methods. Experimental results show that compared with traditional methods, the new model improves the recommendation accuracy by about 15% and the personalization by about 20%. This shows that deep learning has obvious advantages in processing data of large-scale entrepreneurial projects. Further data analysis also shows the training effect and generalization ability of the model. The effectiveness and robustness of the model are verified by analyzing the change trend of training loss and verification loss. Through the survey of user satisfaction, users\u2019 satisfaction with the recommendation system based on deep learning is significantly higher than that of traditional methods.<\/jats:p>","DOI":"10.1177\/14727978251318997","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T15:25:21Z","timestamp":1740065121000},"page":"3397-3410","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of collaborative filtering based on deep learning in college students\u2019 entrepreneurship project recommendation system"],"prefix":"10.1177","volume":"25","author":[{"given":"Mei","family":"Xu","sequence":"first","affiliation":[{"name":"Employment Service Center, Cangzhou Medical College, Cangzhou, China"}]},{"given":"Ling","family":"Yang","sequence":"additional","affiliation":[{"name":"Employment Service Center, Cangzhou Medical College, Cangzhou, China"}]},{"given":"Jun","family":"Zheng","sequence":"additional","affiliation":[{"name":"Modern Education Technology Center, Cangzhou Medical College, Cangzhou, China"}]}],"member":"179","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2019.04.126"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2020.01587"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/1418333"},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Radisic I Lazarevic S. 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