Contrastive learning is a self-supervised learning technique widely used in various fields such as computer vision and natural language processing. Graph contrastive learning (GCL) refers to methods that apply contrastive learning techniques to graph data. A review is presented on the basic concepts, methods, and applications of graph contrastive learning. First, the background and significance of GCL, as well as its basic concepts on graph data, are introduced. Then, the mainstream GCL methods are elaborated in detail, including methods with different graph data augmentation strategies, methods with different graph neural network (GNN) encoder structures, and methods with different contrastive loss objectives. Finally, three research ideas for GCL are proposed. Research findings demonstrate that graph contrastive learning is an effective approach for addressing various downstream tasks, including node classification and graph classification.