Faced with numerous online learning resources, learners often suffer from information overload and information disorientation problems. It has become a hotspot to help learners efficiently and accurately obtain suitable learning resources to improve their learning effects. Considering the deficiencies of existing approaches, such as the poor interpretability as well as the limited efficiency and accuracy of recommendation, a new recommendation approach of personalized learning resources is proposed on the basis of knowledge graphs and graph embeddings. In this approach, a knowledge graph of the online learning environment is established through a generic ontology model, and the graph embedding algorithm is applied to train the knowledge graph for optimized efficiency of graph computation in learning resource recommendation. Then, the learners’ interest in learning resources is optimized via clustering based on the learning style features of learners. Finally, the ranked recommendation results of learning resources are obtained. The experiments demonstrate that the proposed approach significantly improves the computational efficiency and the accuracy of personalized learning resource recommendations compared with existing methods in large-scale graph data scenarios.