Research on Modeling of Government Debt Risk Comprehensive Evaluation Based on Multidimensional Data Mining

Abstract In order to solve the problems of low accuracy of data mining, high relative error rate of evaluation and long time of evaluation in traditional government debt risk evaluation methods, this paper proposes a modeling method of government debt risk comprehensive evaluation based on multidimensional data mining. The MAFIA algorithm is used for multidimensional mining of government debt risk data, and K-means clustering algorithm is used for clustering processing of mined data. According to the clustering results, the KMV model is constructed, and the uncertainty factor is used to modify the model, so as to realize the comprehensive evaluation of government debt risk by using the modified KMV model. The experimental results show that the accuracy rate of government debt risk data mining is always above 91%, the relative error rate of evaluation is always below 3.4%, and the average evaluation time is 0.71s, the practical application effect is good.