Reliability Optimization Analysis of Financial Management System Based on Decision Tree Algorithm

Since the end of last century, data mining technology has emerged in today's scientific research field as an emerging research field. At present, it has been widely used in the fields of finance, retail, telecommunications and enterprise production, and has also produced many commercialized data mining systems. Under the condition of modern market economy and facing the extremely fierce market competition, how to make farsighted strategies and make correct decisions is the top priority related to the success of enterprises and the central issue of modern management. With the process of world economic integration and the development of information technology, and China's accession to the WTO, the domestic financial industry is facing a more complex living environment than ever before. The pressure of competition makes managers more shrewd. They clearly realize that scientific decision-making is the basis of winning. The traditional decision-making methods have been difficult to meet the requirements of the changing business environment. If they want to not be eliminated in the competition, new technologies must be sought. This paper presents a method of national tax financial data analysis based on decision tree algorithm, and introduces the implementation process of this method in detail. The research goal of this paper is to use mature data classification and analysis methods to extract key information from financial data, form decision support information through intelligent processing, and provide it to the leadership, so as to help the leadership better deploy their future work and improve work efficiency. To solve this problem, this paper proposes to use the measurement method to improve the decision tree, extract effective information, help managers make decisions, and carry out experiments with the goal of financial budget management, which has achieved good experimental results and provided strong support for more efficient university management.