There is an issue with the assessment outcomes being unsatisfactory, despite the critical role that intelligent evaluation plays in college students’ mental health. The issue of intelligent evaluation in college students’ mental health cannot be resolved by traditional assessment techniques, and the results are erroneous. Hence, an Apriori method for intelligent mental health evaluation and analysis is proposed in this study. To begin, indications are categorized according to the needs of high-quality intelligent assessments in order to decrease quality distractions in smart assessments; this allows for an intelligent evaluation of college students’ mental health using data mining theory. Following this, an intelligent evaluation method for college students’ mental health is developed using data mining theory. The findings of this assessment are then analyzed and synthesized. Under certain intelligent evaluation conditions, the Apriori algorithm is able to measure the psychological intelligence of college students, according to a MATLAB simulation. Compared to more conventional forms of assessment, intelligent evaluation indicators provide higher levels of accuracy.