Research on Intellectual Dichotomiser 3 Decision Tree Algorithm Model for Financial Analysis of Colleges and Universities

Abstract

The rapid development of college information construction has promoted the processing and analysis of a large number of data in the college system. Decision tree algorithm is often used in the field of financial data analysis, but it has a bias in the selection of attributes. Aiming at the defects of the decision tree algorithm in attribute selection, ID3 algorithm in the decision tree algorithm is selected for weighted improvement, and it is optimized based on Synthetic Minority Oversampling Technique (SMOTE) algorithm and Bagging algorithm to balance the positive and negative data of its training samples, thus obtaining the DSB-ID3 financial analysis model. Using this model to analyze the financial data of a university, its G value and F value are both about 0.78, the recognition accuracy rate for normal samples is 0.7345, and the total recognition accuracy rate is 0.7893, which are the highest among the four models. Compared with other models, model designed in this study has significantly improved classification performance, and its distribution is the most centralized, showing superior stability.The experimental results show that the classification effect of model designed in this study is the best, and it shows superior accuracy and stability in the analysis of financial data. Its superior classification performance shows the potential of decision tree algorithm optimization and the feasibility and necessity of improving it. From the experimental data, it can be seen that the service life and parameters of the model designed in this study are obviously better than those commonly used in the financial analysis industry of colleges and universities. It can be seen from the overall analysis that this model provides a practical reference for the application of decision tree optimization in college financial analysis, and greatly improves the accuracy of financial system data analysis.