In recent years, massive open online courses (MOOCs) have become a significant pathway for acquiring knowledge and skills. However, the increasing number of courses has led to severe information overload. Knowledge concept recommendation aims to identify and recommend specific knowledge points that students need to master. Existing research addresses the challenge of data sparsity by constructing heterogeneous information networks; however, there are limitations in fully leveraging these networks and considering the diverse interactions between learners and knowledge concepts. To address these issues, this study proposes a novel method, heterogeneous learning behavior-aware knowledge concept recommendation (HLB-KCR). First, it uses metapath-based random walks and skip-gram algorithms to generate semantically rich metapath embeddings and optimizes these embeddings through a two-stage enhancement module. Second, a multi-type interaction graph incorporating temporal contextual information is constructed, and a graph neural network (GNN) is employed for message passing to update the nodes, obtaining deep embedded representations that include time and interaction type information. Third, a semantic attention module is introduced to integrate meta-path embeddings with multi-type interaction embeddings. Finally, an extended matrix factorization rating prediction module is used to optimize the recommendation algorithm. Extensive experiments on the large-scale public MOOCCubeX dataset demonstrate the effectiveness and rationality of the HLB-KCR method.