Research on Optimization Algorithm of Enterprise Financial Data Classification Based on Decision Tree

Abstract

The classification and prediction of enterprise financial data can improve the cost and benefit optimization management level of enterprises. In order to improve the ability of enterprise financial data classification, an optimization algorithm of enterprise financial data classification based on decision tree is proposed. The global data model is adopted to establish the management model of enterprise financial database. Based on the heterogeneous parameters among enterprise financial data sources, combined with the structural feature analysis of data sources, the characteristic analysis method of dynamic allocation and correlation constraints of resources such as human, material and financial resources is adopted to establish the allocation model of influencing factors of enterprise financial data. Based on the decision tree classification algorithm, the correlation features of cost and income of enterprise financial data are extracted. According to the pattern change of compliance management income, cluster analysis and pattern recognition of expected income dynamic characteristics of enterprise financial data are realized. By constructing a dynamic allocation model of enterprise financial data and enterprise financial cost and income, cash flow data analysis method is adopted, according to quantitative parameter analysis of realtime operating cash flow, semantic similarity measurement method is adopted, and based on online observation data cleaning, correlation characteristics recognition and cluster analysis of enterprise financial data cost and income are realized, and enterprise financial data is optimally classified. The empirical analysis and simulation results show that this method is highly reliable in classifying enterprise financial data, and has strong ability to dynamically allocate resources such as manpower, material resources and financial resources and control income and cost, thus improving the quality level of enterprise financial data management.