For the curriculum evaluation in colleges and universities, data-driven teaching management and decision-making issues are investigated in this study. First, the index system of curriculum evaluation from a school determines the data structure of multi-dimensional evaluation data covering students, teachers, peer experts, and teaching supervisors. After clean and conversion of the collected questionnaire data, a data set for data mining is constructed. Then, considering misleading suppression, we apply the improved Apriori association rule mining algorithm based on varying interest degrees to extracting the association rules between the evaluation indices. Finally, a comparison of the discovered relational patterns with the results using the traditional Apriori algorithm shows that the improved Apriori method used in this study can increase the efficiency and accuracy of knowledge discovery and has a prominent guiding role in curriculum construction.