Journal of Global Humanities and Social Sciences 2737-5374 2737-5382 BONI FUTURE DIGITAL PUBLISHING CO.,LIMITED https://ojs.bonfuturepress.com/index.php/GHSS/article/view/1531 4 06 2023 2023-12-25 Li Chen,Xiaoling Luo, Sijia Tang, Meina Xie Accurate classification of credit customers is the premise of providing personalized credit services to them. According to customers' credit needs, we collect customer sample data, and then use users' repayment ability and repayment willingness to mark the samples. Bayesian classifier is constructed by constructing probability distribution function. By using test training and testing classification algorithm, it is found that Gaussian Bayesian algorithm can classify and predict data well. In the process of classifying samples, funds are allocated in combination with classification preferences. Experiments show that credit rating parameters have a significant impact on the optimization of resource allocation. By properly setting the values of credit rating parameters and classification preferences, it has reference value for reducing credit risks. Naive Bayes,credit risk,classify,classification preference 10.61360/BoniGHSS232015310604