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At the time of disaster, how to effectively integrate resources of all parties, deal with sudden financial disasters efficiently, and restore financial services in time has become an important task. Therefore, this paper adopts Particle Swarm Optimization (PSO) to improve the traditional BP Neural Network (BPNN) and finally constructs a Particle Swarm Optimization powered BP Neural Network (PSO\u2010BPNN) model for the intelligent emergency risk avoidance of sudden financial disasters in digital economy. At the same time, the proposed algorithm is also compared to GA\u2010BPNN and BPNN algorithms, which are also intelligent algorithms. Experimental results show that the hybrid PSO\u2010BPNN algorithm is superior to GA\u2010BPNN algorithm and BPNN algorithm in simulation and prediction effect. It can accurately predict the sudden financial disaster in recent period, so the model has a good application prospect.<\/jats:p>","DOI":"10.1155\/2021\/7708422","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T18:50:39Z","timestamp":1638298239000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO\u2010BPNN Algorithm"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9500-0809","authenticated-orcid":false,"given":"Lulu","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1108\/jbs-07-2016-0078"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-018-0175-0"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm12020055"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.irle.2017.12.001"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2019.01.011"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-017-2979-z"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.3846\/btp.2019.41"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-25815-w"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2948949"},{"key":"e_1_2_10_10_2","article-title":"Financial risk prediction for listed companies using IPSO-BP neural network","volume":"15","author":"Li S.","year":"2019","journal-title":"International Journal of Performability Engineering"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.02.101"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.2112\/si106-061.1"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0229739"},{"key":"e_1_2_10_14_2","first-page":"14026","article-title":"A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming","volume":"38","author":"Hsu C. 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