This paper utilizes a correlation-based smart prediction model to construct a smart teaching model for mental health. In the ABaisSvd model, the GD algorithm is used to iteratively update the matrix and optimize it by minimizing the error between the reconstructed achievement matrix and the original achievement matrix. The potential impact of attendance behavior and bias factors on grade prediction was also considered, and the prediction function was implemented and optimized after analyzing the correlation between student grades, attendance behavior, and bias factors through dimensionality reduction. According to the analysis of smart psychological teaching practices, 8.1% and 0.65% of students at University X experienced moderate and severe psychological problems, respectively. The smart classroom achieved a score of more than 87 points for the dimensions of appropriate use of educational techniques and obvious educational effects. The level of smart classroom and mental health in Group I was significantly higher than before the experimental intervention. There was no significant change in Group II before and after the intervention. Still, the level of mental health after the intervention (1.75) was also slightly higher than that before the intervention (1.74), and more than half of the students got a sense of theoretical, practical, and emotional gain in wisdom teaching. To sum up, the wisdom education model in this paper has the potential to significantly improve the mental health of college students.