Abstract: In order to study the mastery of knowledge points at different stages of student,based on the theory of data mining,knowledge structure was combined with examination results to study data.Based on the theory of educational measurement and the decision tree algorithm of data mining,an improved algorithm was proposed according to the original C4.5 algorithm,applying the difficulty level of the knowledge points involved in the test papers and the knowledge structure to refine the knowledge structure in order to determine the degree of knowledge of individual students or groups of students and the relationship between the knowledge points.The experimental results show that the efficiency of the improved algorithm is improved,whose formula is simple and practical compared with the original formula.According to the decision tree model,the remaining data is used to verify the improved formula,and it is faster to draw the conclusion that the effect of knowledge points on programming is relatively important.Test data is used to verify the decision tree,and the accuracy rate is 90%.Finally,a visual display of the decision tree can give an effective reference for students to learn the arrangements,teachers to develop teaching programs and arrangements.

Key words: C4.5, Data mining, Decision tree, Knowledge structure, Paper analysis