Advances in big data technology herald a new era for mental health education in higher education, offering novel solutions to age-old challenges in student psychological care. This paper investigates how big data’s analytical capabilities can surpass traditional, one-size-fits-all mental health assessments by leveraging detailed student data for personalized care. We applied data mining techniques to the mental health data of 1,200 students from College G, creating a rich database of psychological patterns and a mental health information exchange platform. The analysis led to the identification of 500 instances of psychological distress with an accuracy of 79.5% and unveiled patterns linking academic stress and adjustment difficulties to mental health issues. Our research underscores big data’s role in enhancing mental health interventions, providing the groundwork for more individualized and effective mental health services in academic settings.