{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:22:30Z","timestamp":1771003350623,"version":"3.50.1"},"reference-count":18,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024,1]]},"abstract":"<jats:p>The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&amp;P 500 index appeared in the four plunge, and led to the market trading meltdown, the world\u2019s financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company\u2019s stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public\u2019s awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaotic particle swarm algorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaotic particle swarm optimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaotic particle swarm optimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions.<\/jats:p>","DOI":"10.3233\/jcm-237119","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T10:19:34Z","timestamp":1709893174000},"page":"105-120","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Validation study of Yunnan ethnic culture industry stock model under epidemic situation based on chaotic particle swarm optimization neural networks"],"prefix":"10.1177","volume":"24","author":[{"given":"Jiaying","family":"Jiang","sequence":"first","affiliation":[{"name":"Hunan University of Science and Technology","place":["China"]}]},{"given":"Zhixiu","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan University of Science and Technology","place":["China"]}]},{"given":"Sha","family":"Tao","sequence":"additional","affiliation":[{"name":"Hunan University of Science and Technology","place":["China"]}]},{"given":"Xinyi","family":"Tan","sequence":"additional","affiliation":[{"name":"Hunan University of Science and Technology","place":["China"]}]},{"given":"Ying","family":"He","sequence":"additional","affiliation":[{"name":"Hunan University of Science and Technology","place":["China"]}]},{"given":"Wenchao","family":"Pan","sequence":"additional","affiliation":[{"name":"Hunan University of Science and Technology","place":["China"]}]}],"member":"179","published-online":{"date-parts":[[2024,3]]},"reference":[{"issue":"05","key":"e_1_3_2_2_2","first-page":"24","article-title":"A study on the communication status of intangible cultural heritage based on factor analysis and BP neural network: A case study of Shaanxi Province","volume":"44","author":"Cao HB","year":"2023","unstructured":"CaoHB PengJR HuiJJ. A study on the communication status of intangible cultural heritage based on factor analysis and BP neural network: A case study of Shaanxi Province. Journal of Capital Normal University (Natural Science Edition). 2023; 44(05): 24-29.","journal-title":"Journal of Capital Normal University (Natural Science Edition)."},{"key":"e_1_3_2_3_2","unstructured":"DingQP. Research on financial risk detection of science and innovation board enterprises based on BP neural network. Jilin University. 2023."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2006.329691"},{"issue":"03","key":"e_1_3_2_5_2","first-page":"76","article-title":"Ultrashort-term wind speed prediction based on improved particle swarm optimization neural network","volume":"36","author":"Duan XH","year":"2021","unstructured":"DuanXH LiWX. Ultrashort-term wind speed prediction based on improved particle swarm optimization neural network. 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Collaborative filtering recommendation based on chaotic particle swarm clustering optimization. Computer Engineering and Design. 2021; 42(8): 2173-2179.","journal-title":"Computer Engineering and Design."},{"issue":"11","key":"e_1_3_2_12_2","first-page":"37","article-title":"Research and application of intelligent power technology based on BP neural network metro shield","volume":"23","author":"Liu T","year":"2023","unstructured":"LiuT WangS WuJJ, et al. Research and application of intelligent power technology based on BP neural network metro shield. China Water Transportation (the Second Half of the Month). 2023; 23(11): 37-39.","journal-title":"China Water Transportation (the Second Half of the Month)."},{"issue":"9","key":"e_1_3_2_13_2","first-page":"115","article-title":"A rough set continuous attribute discretization algorithm for particle swarm optimization BP neural network","volume":"0","author":"Mao MY","year":"2023","unstructured":"MaoMY XuSC. 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