This paper proposes a computer-aided teaching model using knowledge graph construction and learning path recommendation. It first creates a multimodal knowledge graph to illustrate complex relationships among knowledge. Learning elements and sequences are then used to form time sequences stored as directed graphs, supporting flexible path recommendations. Learners select elements based on interests and learning bases, updating behavior data for precise path recommendations. The platform, employing distributed architecture, integrates data processing and teaching applications for comprehensive cycle management and assessment. Controlled experiments validate its efficacy in enhancing learning outcomes compared to traditional methods, catering to personalized learning needs and advancing intelligent teaching.