Graph neural network (GNN) is used to process graph structure data based on deep learning techniques. It combines graph propagation operations with deep learning algorithms to fully utilize graph structure information and vertex features in the learning process. GNNs have been widely used in a range of applications, such as node classification, graph classification, and link prediction, showing promised effectiveness and interpretability. However, the existing deep learning frameworks (such as TensorFlow and PyTorch) do not provide efficient storage support and message passing support for GNN’s training, which limits its usage on large-scale graph data. At present, a number of large-scale GNN systems have been designed by considering the data characteristics of graph structure and the computational characteristics of GNNs. This study first briefly reviews the GNNs, and summarizes the challenges that need to be faced in designing GNN systems. Then, the existing work on GNN training systems is reviewed, and these systems are analyzed from multiple aspects such as system architecture, programming model, message passing optimization, graph partitioning strategy and communication optimization. Finally, several open source GNN systems are chosen for experimental evaluation to compare these systems in terms of accuracy, efficiency, and scalability.