Students’ psychological states shape how ideological and political education works in higher education, yet many existing approaches still depend on fixed teaching arrangements and relatively broad interventions. In practice, differences in emotion, engagement, and content acceptance are common, and they rarely remain unchanged across time. Under these conditions, static strategies are often insufficient. This paper proposes a data-driven adaptive framework that uses educational and behavioral data to identify psychological changes and adjust intervention decisions in time. The framework supports individualized intervention design, flexible content delivery, and response updating under changing student conditions. Rather than following a fixed intervention path, the proposed method makes decisions from evolving data signals and predicted outcomes. Experimental results show that the method achieves 91.02% accuracy on the Ideological Education Impact Dataset. This result indicates that the framework can align educational intervention more closely with students’ psychological changes and provides a more practical basis for adaptive ideological and political education in higher education.
