A pre-training method based on a dynamic graph neural network learns node representation from three aspects of time, structure and semantics, and comprises the following steps: determining the size of a sampling sub-graph according to actual requirements and system performance, and performing sub-graph sampling on large-scale dynamic graph data by using a time sensitive sampling algorithm to obtain a sub-graph; for the sub-graph, performing covering processing on the sub-graph by using a time-sensitive edge covering algorithm and a node feature covering algorithm to obtain a processed new sub-graph; a dynamic graph generation algorithm is combined with a GNN model to predict covering edges and covering node features of the sub-graphs, optimal parameters are stored, and the pre-training process is ended; and loading the optimal parameters, and performing fine adjustment on the predicted graph data according to different downstream tasks to obtain a final result. According to the method, large-scale dynamic graph data can be processed, and compared with other pre-training methods, the method is higher in expression ability, learned nodes are more accurate in expression, and the method can be better applied to various downstream tasks.