Abstract: With the advancement of educational informatization, traditional chemistry teaching models are increasingly challenged by issues such as knowledge fragmentation, ambiguous learning pathways, and insufficient focus on student-centered learning. Addressing these challenges necessitates the application of knowledge graph technology to facilitate systematic knowledge integration, adaptive learning, and personalized teaching support. This study focuses on two key aspects: the development of a course knowledge graph for university chemistry and the implementation of curriculum reform. Leveraging the capabilities of the knowledge graph in data analysis, intelligent recommendation, and pathway planning, a five-stage progressive teaching model—“predict, test, learn, practice, guide”—has been established. This model enables personalized teaching through “pre-class profiling and precise strategy formulation, in-class focus on diverse teaching methods to enhance competencies, and post-class personalized practice evaluation and intelligent tutoring”. The findings offer novel insights and practical approaches for university chemistry curriculum reform, providing theoretical guidance for adaptive learning models for students and personalized teaching strategies for educators, thereby driving the evolution of courses towards greater intelligence and digitalization.

Key words: Course knowledge graph, University chemistry, Curriculum reform, Teaching model, Teaching strategy