Summary: In recent years, with the emergence of massive data, graph structure data that can represent complex relationships between objects has received more and more attention and has brought great challenges to existing algorithms. As a deep topology information can be revealed, graph neural network models have been widely used in many fields such as communication, life sciences, and finance. This paper reviews the basic models, algorithms, applications and recent developments of existing graph neural networks in recent years, and proposes problems for further research.