The current large-scale teaching model in Chinese universities struggles to accommodate individual student differences, resulting in delayed and imprecise traditional learning interventions that fail to meet the urgent demand for personalized talent development in the era of intelligent education. This study proposes a large model-driven precision learning intervention framework. By integrating multimodal data, student profiling tags, and course knowledge graphs, the model enables granular cognitive diagnostics of students’ knowledge gaps. Leveraging large models for natural language generation, it generates personalized intervention strategies, effectively transforming teaching paradigms from “mass-scale” to “personalized.” This approach provides crucial theoretical guidance for universities to establish precision teaching intervention systems and enhance talent cultivation quality.