Knowledge graph (KG) is a kind of technology that uses graph model to describe the relationship between knowledge and modeling things. Knowledge graph embedding (KGE), as a widely adopted knowledge representation method, its main idea is to embed entities and relationships in a knowledge graph into a continuous vector space, which is used to simplify operations while preserving the intrinsic structure of the KG. It can benefit a variety of downstream tasks, such as KG completion, relation extraction, etc. Firstly, the existing knowledge graph embedding technologies are comprehensively reviewed, including not only techniques using the facts observed in KG for embedding, but also dynamic KG embedding methods that add time dimensions, as well as KG embedding technologies that integrate multi-source information. The relevant models are analyzed, compared and summarized from the perspectives of entity embedding, relation embedding and scoring functions. Then, typical applications of KG embedding technologies in downstream tasks are briefly introduced, including question answering systems, recommendation systems and relationship extraction. Finally, the challenges of knowledge graph embedding are expounded, and the future research directions are prospected.