With the rapid advancement in smart city infrastructures, the demand for personalized and explainable educational services has become increasingly prominent. To address the challenges of information overload and the lack of interpretability in traditional learning resource recommendation, this paper proposes a Structure-aware and Attention-enhanced explainable learning resource Recommendation approach (StAR) for smart education. StAR constructs a reinforcement learning framework grounded in a knowledge graph to model learner–resource interactions. First, a multi-head attention mechanism encodes path states and extracts key semantic features, enhancing the model’s ability to represent complex learning contexts. Then, a dual-layer action pruning strategy compresses the action space and improves reasoning efficiency. Finally, a structure-aware reward function guides the generation of semantically coherent and interpretable recommendation paths. Experiments on two real-world educational datasets, COCO and MoocCube, demonstrate that StAR outperforms several baseline models, achieving improvements of 14.2% and 12.6% in NDCG and Recall on COCO, and 5.2% and 4.2% on MoocCube, respectively. The results validate the effectiveness of StAR in enhancing recommendation accuracy, reasoning efficiency, and interpretability, offering a promising AI-enhanced solution for personalized learning in smart cities.