How to build a complete early warning rule repository is a key issue in the research on the early warning of university student achievements. In this study, after cleaning and discretizing the data on university student achievements, we use the Apriori algorithm to mine the correlation between failed courses and construct the basic early warning rule repository. On this basis, the influence of courses with “pass” and “good” grades are explored to further expand the early warning rule repository. In the case of copious redundant rules, strong association rules are filtered out by lift and interest in the traditional support-confidence framework to improve the accuracy of the repository and specifically analyze the mined rules. Our methods and conclusions can support the decision-making of teaching management.