Educational recommendations empower personalized learning by suggesting suitable learning resources to learners, and the graph-based recommenders are widely adopted. Existing methods are mainly ID-based, which initialize learners and resources with trainable identifiers and optimize their representations solely from the interaction graph. As a result, the lack of semantic understanding of learning resources and learning paths hinders further improvements in recommendation accuracy. To alleviate the problem, we propose KLU4EduRec, which leverages large language models (LLMs) to understand resource knowledge and learning paths, thereby enhancing traditional graph-based educational recommenders. Specifically, for learning path understanding, we segment a learning path by detecting learning pattern drift in resource knowledge sequence, and prompt LLMs to infer learners' learning patterns within each segment. The segment-level patterns are then chronologically aggregated to represent the overall learning path. Besides, we prompt LLMs to summarize the core knowledge of learning resources from their content as complementary semantic signals. Finally, the resulting semantic representations are aligned and fused with structural representations learned by a graph-based recommender to enable more accurate recommendations. Extensive experiments show that KLU4EduRec greatly outperforms existing methods, including traditional ID-based methods and recent LLM-powered methods. A case study shows how the understanding of pattern drift in a learning path leads to more suitable recommendations. Our codes are available at https://github.com/DaSESmartEdu/KLU4EduRec.