Research on Economic Risk Automatic Classification Model Based On Bayes Classification Algorithm

Aiming at the economic risk assessment problem of Dalian City, this paper uses historical data to train a Bayesian model, classifies the current economic indicator data into different risk levels, and calculates a comprehensive economic risk index according to the occurrence probability and potential loss of each risk category. The experimental results show that the algorithm can effectively identify and evaluate the economic risk status of Dalian City, provide valuable information support for local government decision-making, and provide a feasible method for solving complex economic risk assessment problems by applying Bayesian classifier. Compared with traditional methods that rely only on a single strategy, naive Bayes classification algorithm performed best in economic risk assessment, with an accuracy of 95.2%, a recall rate of 93.6%, and an accuracy of 96.4%. The F1 Score was 95%, while the accuracy of Logistic regression, support vector machine and random forest algorithm was relatively low, which were 81.7%, 83.1% and 84.6% respectively, indicating that this algorithm showed significant optimization effects in different dimensions.