Abstract: Using the Analytical Chemistry course as a case study, this paper investigates the construction of knowledge graph-based teaching resources and explores practical approaches for intelligent teaching implementation. The study demonstrates that knowledge graphs enable personalized learning pathways and tailored resource recommendations by analyzing student learning data, thereby enhancing self-directed learning capabilities. Furthermore, real-time feedback mechanisms facilitate targeted instructor interventions and content optimization. The visual representation of knowledge graphs improves knowledge integration and instructional design, enabling data-driven adaptive learning and offering innovative perspectives for educational reform.

Key words: Knowledge graph, Analytical chemistry, Intelligent teaching, Adaptive learning