Abstract In the digital era, the rapid development of information technology has not only changed the education model but also provided new tools and paths for education management. This paper establishes the two most commonly used metrics of support and confidence for association rule analysis. Using the Apriori algorithm, all frequent items in the dataset that satisfy the mining conditions are extracted, and through the constraints of the metrics, the eligible association rules are screened, and the association rule results are parsed. Add the interest degree model to reduce the generation of non-interesting rules. The cluster analysis algorithm and it are combined to apply it in the field of education management, which is empirically examined. The results of cluster analysis and association rule mining show that the support degree of rule 1 is 0.4925, and the confidence level is 1, indicating that if 49.25% of all students score between 2 and 4 in question 3, then the score in question 1 is similar to the score in question 3. Based on the weights calculated by the association rules, the teachers’ basic teaching literacy, classroom teaching ability, teaching and research ability, professional ability and after-school counseling were evaluated respectively, and the overall score of teacher 1 was the highest, with a score of 91.213, which accounted for 10% of the total number of students, and the evaluation method adopted in this paper was scientific and fair.
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