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Knowledge graphs have a wide range of applications in the field of artificial intelligence, such as information retrieval, natural language processing, recommender systems, etc. However, the openness of knowledge graphs often means that they are incomplete and have their own flaws. In view of this, it is necessary to establish a more complete knowledge graph to improve the actual utilization of the knowledge graph. Using link prediction to infer new relations through existing relations, so as to realize the completion of large-scale knowledge base. By comparing the knowledge graph link prediction models based on translation models, the framework of the knowledge graph link prediction model is analyzed from the aspects of commonly used datasets and evaluation indicators, translation models, and sampling methods, and the link prediction models based on knowledge graphs are reviewed.