In the context of China’s higher education system, the performance in public foundation courses serves as a critical bridge for freshmen transitioning to upper grades, reflecting students’ learning foundations. Teachers pay close attention to difficulties encountered by students in these courses, which have limited potential for score improvement due to various influencing factors. As the number of university students increases, traditional statistical methods for analyzing student performance become less effective. This paper explores the factors affecting student performance through data mining techniques, employing a decision tree algorithm to design a system for analyzing and predicting the impact on public foundation course scores. The goal is to enhance scores effectively, assist teachers in providing targeted instruction, and address students’ learning challenges.