Big data technology has injected new vitality into the education industry, but traditional data analysis methods are still insufficient in talent cultivation. This chapter innovatively applies association rule mining technology to optimize talent-training programs in universities to meet practical needs. We collected student data from a computer college of a certain university, cleaned, transformed, and reduced it, and applied association rule mining algorithms to deeply analyze the correlation between courses. Based on the mining results, we have developed a precise talent cultivation plan. Through empirical research, it was found that the average score on the C language test for the top 16 students in Class 1 under this method can reach 92 points, and the average score of skill level is also 85.55 points. The average score of the C language test for the top 16 students in Class 2 under traditional methods is 85.66 points, while the average score of their skill level is only 76.56 points. After using the method described in this chapter, the skill level of students has significantly improved, and the targeted talent cultivation has also been significantly enhanced. This study not only demonstrates the feasibility of applying association rule mining to talent-training programs but also innovates data-driven models in the field of education.