Design And Implementation Of Financial Data Analysis Management System For Internal Revenue Service

With the development of science and technology,the tax system is also gradually keep pace with technological developments,the financial management system is being used as an administrative management platform,basically used to handle the daily work of financial accounting,financial data is used in large centralized model,with the frequent use of the system every day provincial bureau database Will produce large amounts of business data,such historical data usually used only as a summary query,data availability is not high,wasting a lot of resources,more important is not to provide effective leadership in decision-making support.For better use of existing data of IRS financial management system,this paper puts forward to design and implementation a system,that through to introduce the existing data analysis technology and the analysis of the financial management software,design and develop a financial data analysis system of the Internal Revenue Service.According to the research target,the decision tree algorithm to construct an classifier are used in the data analysis module design and implementation.The subject of the decision tree classification algorithm used is the use of large amounts of data in the training set to create multiple classifier,and then use a simple scoring weight voting algorithm,selected the best classification results,for the large-scale experimental evaluation shows data set,the algorithm has good classification performance.Through the decision tree algorithm,the experimental platform on three public data sets and three sets of financial expenditure categories of data validate the accuracy of prediction and classification.Through design and verification,the ultimate realization is can use sophisticated data classification analysis methods to extract financial data from the critical tax information,through the intelligent processing,decision support information to the formation of leadership,helping leaders to better deployment of future work and improve efficiency.