To effectively analyze college physical fitness test data and quickly feed back the factors that affect students’ test results, this study takes the physical fitness test data of the North University of China as the sample and transforms preprocessed data into datasets suitable for data mining. Considering the limited features and consistent length of physical fitness test data, an Apriori algorithm that combines the transaction reduction technique with the hash technique is used to analyze data, which reduces the number of database traversal and the scale of candidate sets generated. It also improves the efficiency of the algorithm and ensures mining accuracy at the same time. Finally, comparison and analysis are made with the Apriori algorithm, the Apriori algorithm based on transaction reduction, and the Apriori algorithm based on the hash technique. The experimental results show that the proposed improved Apriori algorithm that combines transaction reduction and the hash technique can effectively analyze the association rules among students’ physical fitness test results and therefore has a stronger guiding significance for students’ physical fitness training. Compared with the Apriori algorithm, the proposed algorithm improves the operation efficiency by more than 85%.