Wind speed prediction is an important factor affecting the efficiency and stability of wind farms. Based on the spatio-temporal features of wind speed, the VMD-based hybrid spatio-temporal network (VHSTN) integrates variational modal decomposition (VMD) and hybrid deep learning framework to predict the short-term wind speed. The hybrid deep learning framework is composed of convolutional neural network (CNN), long and short-term memory (LSTM), and self-attention mechanism (SAM). After the cleaning of raw data, the VMD is employed to decompose the spatio-temporal data of wind speed for multiple sites into intrinsic mode functions (IMF) components, eliminating the instability of the wind speed data. For each IMF component, the spatial features are extracted by the CNN at the bottom of the model. Next, the temporal features are captured by the top-level LSTM. Then, SAM is applied to strengthen the extraction of key hidden features through adaptive weighting and obtain the prediction results of each component. Finally, the results are amalgamated to determine the final predicted wind speed. Experiments are conducted on the commonly used dataset WIND in this study. The experimental results prove the effectiveness and superiority of the proposed algorithm compared with related typical algorithms.