Teaching Quality Evaluation and Software Implementation Based on ID3 Decision Tree Algorithm

As a classical decision tree algorithm, ID3 selects the best test attribute based on information entropy, uses information gain as the attribute division basis, and selects the attribute with the largest information gain as the split node to generate a decision tree. ID3 algorithm is simple, clear and easy to understand, and has very high classification efficiency. This paper mainly includes three aspects. Firstly, study ID3 decision tree algorithm, including basic algorithm and algorithm improvement. Secondly, it constructs the evaluation index system of teaching quality. Thirdly, the realization of teaching quality evaluation software based on ID3 decision tree algorithm is studied, which consists of data acquisition, data preprocessing, selection of optimal features, generation of decision tree model, evaluation of decision tree model, teaching quality evaluation and visualization of evaluation results.