大学数学知识抽象、逻辑严密、关联复杂，传统模式难以刻画知识点联系，易致碎片化、推理路径模糊与迁移不足。为实现精准解析与智能推理，该文以高数、线代、概率论为对象，提出融合知识图谱、图神经网络与大语言模型的关联与推理路径挖掘：构建含实体、关系、属性的图谱；从结构语义逻辑量化关联强度识别核心与隐性关联；基于双路径图推理与大语言模型逻辑校验生成最短最优逻辑多分支路径。实验验证该方法精度更高且可解释性强，可为教学设计、个性化路径规划及智能教育系统提供理论支撑与技术参考。

University mathematics involves abstract concepts, rigorous logic, and complex interrelations among knowledge points. Traditional teaching methods are inadequate for capturing these connections, often resulting in fragmented knowledge, ambiguous reasoning paths, and poor knowledge transfer. To achieve accurate knowledge analysis and intelligent reasoning, this paper focuses on three core subjects—Calculus, Linear Algebra, and Probability Theory—and proposes an approach that integrates knowledge graphs, graph neural networks, and large language models for correlation and reasoning path mining. Specifically, a knowledge graph containing entities, relations, and attributes is constructed. The correlation strength between knowledge points is quantified from structural, semantic, and logical perspectives to identify both core and implicit associations. Subsequently, through dual-path graph reasoning combined with logical verification using a large language model, shortest, optimal, and multi-branch reasoning paths are generated. Experimental results demonstrate that the proposed method achieves higher accuracy and strong interpretability, providing theoretical support and technical reference for instructional design, personalized learning path planning, and intelligent education systems.