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. This study tries 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, the key ideas, algorithm features, advantages and disadvantages of different embedding models are introduced and analyzed to give the first-time researchers a guideline that can be referenced to help researchers quickly get started.