With the increasing scale of network, the accurate and real-time prediction of network flow is essential for traffic scheduling and routing design. However, due to the nonlinearity and uncertainty of network flow data, some traditional methods fail to achieve good prediction accuracy. Considering the complex spatialtemporal features of network flow, a novel network flow prediction method based on spatialtemporal features fusion (ST-Fusion) was proposed, combined with encoderdecoder architecture. First, the encoder was designed with two parallel feature channels: temporal and spatial channels. The temporal features were extracted by integrating gated recurrent unit (GRU) and self-attention mechanism, and the graph convolutional network (GCN) was used to extract the spatial features. Then, the temporal and spatial features extracted by the encoder were fused by using the bilateral gated mechanism. Finally, the fused features were input into the GRU-based decoder to generate prediction results. Experiments were conducted on three public datasets (GEANT, ABILENE, and CERNET) using evaluation metrics including MAE, RMSE, ACCURACY, and VAR. Experimental results showed that the ST-Fusion method achieved better performance in network flow prediction.