Knowledge Graphs (KGs) serve as a kind of knowledge base by storing facts with network structure, representing each piece of fact as a triple, i.e. (head, relation, tail). Thanks to the general applications of KGs in various of fields, the embedding learning of Knowledge Graph has also quickly gained massive attention. In this article, we try to classify the existing embedding algorithms as five types:translation-based models, tensor factorization-based models, traditional deep learning-based models, graph neural network-based models and models by fusing extra information. Then we introduce and analyze the key ideas, algorithm features, advantages and disadvantages of different embedding models to give the first-time researchers a guide article that can be referenced to help researchers quickly get started.