Video salient object detection (VSOD) can continuously locate motion-related salient objects in video sequences by combining spatial and temporal information. Its core lies in how to efficiently describe the spatial and temporal features of moving objects. Existing VSOD algorithms mainly use optical flow, ConvLSTM, and 3D convolution to extract time domain features, but their continuous learning ability of temporal information is insufficient. Therefore, a robust spatial-temporal progressive learning network (STPLNet) is proposed to realize the efficient localization of salient objects in the video sequences. In the spatial domain, the method uses a U-shaped structure to encode and decode each video frame. In the temporal domain, it progressively encodes the features of the moving objects by learning the features of subject parts and deformation regions about the moving objects between frames in the video sequences. In this way, the method can capture the time correlation features and motion tendency of the objects. A series of comparative experiments are carried out on four public datasets, with 13 mainstream VSOD algorithms involved. The proposed model achieves optimal results on multiple indicators including maxF, S-measure (S), and MAE, and it has excellent real-time performance in running speed.