At present, big data regarding education are increasingly growing. How to efficiently and accurately extract high-value knowledge from the massive data to meet the personalized education needs of learners or educators is a hot topic worthy of attention in smart education. As a visual analysis technology, knowledge graphs can effectively construct and mine knowledge and the interrelationship between knowledge, which has been successfully applied in many fields. The introduction of graph embedding technology is beneficial to significantly improve the processing efficiency of knowledge graphs in the context of big data. To meet the knowledge processing needs of personalized education, this paper first introduces the basic concepts of knowledge graph and graph embedding algorithms and then expounds the triple-based representation learning model from three aspects: vector translation, tensor-based factorization, and neural network-based representation learning. Then, from the perspective of seven application types, the practical application of knowledge graph and graph embedding in the field of personalized education is reviewed. Finally, the paper is summarized and the directions of future research are discussed.