{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T22:47:59Z","timestamp":1784069279114,"version":"3.55.0"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"vor","delay-in-days":277,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72161033"],"award-info":[{"award-number":["72161033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004763","name":"Natural Science Foundation of Inner Mongolia","doi-asserted-by":"publisher","award":["2025MS07001"],"award-info":[{"award-number":["2025MS07001"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>For the credit risk problem, an artificial intelligence credit risk assessment model based on multilayer perceptron (MLP) hybrid clustering is proposed. This study is the first to evaluate credit risk evaluation indexes based on a combination of natural language processing (NLP), cluster analysis, and correlation analysis and to construct a credit risk evaluation index system. The weights of the indicators are then determined through the application of the analytic hierarchy process (AHP) and the criteria importance through intercriteria correlation (CRITIC) assignment method; then, the weighted credit evaluation indicator data are used to construct a credit risk assessment model based on the MLP to predict the credit status of the business entity, and then a Gaussian mixture model (GMM) combined with the expectation\u2013maximization (EM) algorithm is employed to divide the credit risk levels into intervals. Finally, based on the research information of 246 family farms and ranches in 12 cities in the Inner Mongolia region, an empirical analysis is conducted. The results demonstrate that (1) most of the investigated samples of family farms in Inner Mongolia are in a low\u2010risk state, which follows the law of large numbers and practical significance. (2) The accuracy of the regression model to predict the credit risk of family farms in Inner Mongolia is examined with the ROC curve, and the results show that the AUC value is 0.92, and the MLP model fits well.<\/jats:p>","DOI":"10.1155\/cplx\/3308222","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T05:20:04Z","timestamp":1759728004000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence Credit Risk Assessment Model Based on MLP\u2010Hybrid Clustering"],"prefix":"10.1155","volume":"2025","author":[{"given":"Pengzhe","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfang","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyun","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiao","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7884-5826","authenticated-orcid":false,"given":"Zhanjiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,5]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"97","article-title":"The Application of Factor Analysis in Wall Scoring Method","volume":"5","author":"Fan X. 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A Review of Artificial Intelligence Approach for Credit Risk Assessment 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) 2022 Vijayawada India 1\u20135 https:\/\/doi.org\/10.1109\/aisp53593.2022.9760655593.2022.9760655.","DOI":"10.1109\/AISP53593.2022.9760655"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2021.06.053"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.seps.2023.101746"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm16120496"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.13714\/j.cnki.1002-3100"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1142\/S2424786322500153"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.5334\/joc.381"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.13287\/j.1001-9332"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3445308"},{"key":"e_1_2_10_18_2","first-page":"91","article-title":"Credit Risk Assessment of SME Supply Chain Finance Based on MLP Neural Network","volume":"36","author":"Chen X. Z.","year":"2021","journal-title":"Journal of Hunan University of Science &Technology (Social Science Edition)"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2022.3161766"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-81-322-3972-7_19"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5381208"},{"key":"e_1_2_10_22_2","volume-title":"Research on the Evaluation of decision-making of Microfinance for Farmers Based on Support Vector Machine","author":"Cheng Y. 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