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With the continuous development and maturity of machine learning technology, it has played an important role in the field of data prediction and risk assessment. This paper conducts research on the application of machine learning technology in enterprise risk assessment. According to the existing literature, this paper uses three machine learning algorithms, i.e., random forest (RF), support vector machine (SVM), and AdaBoost, to evaluate enterprise risk. In the specific implementation, the enterprise\u2019s risk assessment indexes are first established, which comprehensively describe the various risks faced by the enterprise through a number of parameters. Then, the three types of machine learning algorithms are trained based on historical data to build a risk assessment model. Finally, for a set of risk indicators obtained under current conditions, the risk index is output through the risk assessment model. In the experiment, some actual data are used to analyze and verify the method, and the results show that the proposed three types of machine learning algorithms can effectively evaluate enterprise risks.<\/jats:p>","DOI":"10.1155\/2021\/6049195","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T02:50:06Z","timestamp":1637117406000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Enterprise Risk Assessment Based on Machine Learning"],"prefix":"10.1155","volume":"2021","author":[{"given":"Boning","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junkang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhong","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1672-2887","authenticated-orcid":false,"given":"Chang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnsc.2008.12.010"},{"key":"e_1_2_10_2_2","first-page":"1008","article-title":"Unsupervised quadratic surface support vector machine with application to credit risk assessment","author":"Jian Luo a","year":"2020","journal-title":"European Journal of Operational Research"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113567"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105936"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106329"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1023\/a:1010933404324"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.06.069"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2005.50"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.2014.2318020"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsyst.2013.2294634"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2932756"},{"key":"e_1_2_10_12_2","doi-asserted-by":"crossref","unstructured":"QuZ. 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