As the relationship between college students’ extracurricular exercise behaviors and physical health is getting more and more attention, how to improve college students’ physical health through effective exercise behaviors has become a hot research topic. In this paper, the temporal association rules between college students’ extracurricular exercise behavior and physical health and sports performance are mined by applying Apriori algorithm. The study firstly collected the physical examination data and body side performance data of college students in a university, which included height, weight, body mass index, flexibility, cardiorespiratory function and other health indicators, and pre-processed the data with the athletic behavior performance of college students. The results of the study show that male college students have poor flexibility, medium reaction time and poor cardiorespiratory fitness in the “anticipation stage”, which directly affect their physical fitness level. Through data mining, we obtained 10 rules with decision-making significance. Among female college students, those with superior flexibility, poor cardiorespiratory fitness, and higher body mass index need to pay more attention to strength and endurance training. With the improved Apriori algorithm, the study not only improved the efficiency of data mining, but also expanded the mining scope of association rules and found more valuable associations between exercise behavior and health status. These findings provide a scientific basis for the development of sports interventions for college students.
