The popularization of large-scale online education has made it difficult for learners to choose courses, and personalized learning path recommendation faces the challenge of relying on single modal data, which leads to the limitation of semantic representation. Moreover, static knowledge maps are difficult to generate dynamic explainable recommendation logic. To address the aforementioned issues, this paper proposed a framework of explainable learning path recommendation based on dynamic attention reinforcement learning (ELPR-DARL). Firstly, a heterogeneous collaborative knowledge graph was constructed, integrating course text, visual content, and knowledge dependencies to enhance cross-modal semantic alignment capabilities. Secondly, a dynamic attention aggregation mechanism for adjacent nodes was designed, which adjusts the weights of entity relationships through a bias correction strategy, and a bidirectional interaction aggregator was utilized to fuse multi-level neighborhood features, enhancing the fine-grained expression ability of knowledge reasoning. Finally, a knowledge graph-aware reinforcement learning strategy was proposed, which explicitly modelled the association between user behavior and knowledge topology based on path connectivity reward functions, generating explainable paths that include global rewards and local attention weights. Experiments based on the MOOC dataset show that this method achieves 22.85%, 33.81%, 52.01%, and 6.34% in NDCG, Recall, HR, and precision metrics, respectively, which is 2.88%, 3.55%, 2.42%, and 3.26% higher than the suboptimal model. User research shows that 80.36% of learners believe that path explanation significantly improves recommendation transparency. This study verifies that the collaborative optimization of a dynamic attention mechanism and reinforcement learning can effectively balance recommendation accuracy and explainability.
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