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In order to effectively identify and prevent fraudulent users on e-commerce websites, a research proposes an automatic identification method on the basis of Random Forest algorithm. It achieves automatic detection of fraudulent behavior by analyzing the behavioral characteristics and user activity patterns of the website. The research results demonstrated that the accuracy on the experimental dataset is as high as 98.76%, with a recall rate of 97.53%, which is much higher than other methods and demonstrates extremely high recognition performance. This method can also effectively identify different types of fraudulent behavior, with a recognition rate of over 98%. This result further confirms the advantages of the Random Forest algorithm in dealing with imbalanced datasets and complex classification problems. Research can help combat online fraud and promote the healthy development of the e-commerce industry.<\/jats:p>","DOI":"10.1177\/14727978241295575","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T03:15:24Z","timestamp":1745896524000},"page":"1147-1154","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic identification method for fraudulent users on e-commerce websites based on Random Forest algorithm"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1608-9575","authenticated-orcid":false,"given":"Jingbo","family":"Zhai","sequence":"first","affiliation":[{"name":"School of International Business and Management, Shanghai Sipo Polytechnic, Shanghai, China"}]}],"member":"179","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"issue":"1","key":"e_1_3_3_2_2","first-page":"1","article-title":"Enhancing E-commerce security: The effectiveness of blockchain technology in protecting against fraudulent transactions","volume":"7","author":"Dahal SB","year":"2023","unstructured":"Dahal SB. 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