The resolution of a power monitoring video is sometimes problematically low. Thus a super-resolution reconstruction model of such a video based on deep learning is proposed. First, a convolutional neural network is used to extract features of the input video frames. All support frames are aligned to the target frame from the two ends of the input video sequence in chronological order, and a time and space self-attention mechanism is used to fuse the support frame features to the target frame by frame. Secondly, a multi-scale feature learning mechanism is introduced, and a multi-branch feature selection reconstruction network is constructed to perform super-resolution reconstruction of the target frame after feature fusion. Finally, the model is trained with public datasets and power video datasets. The results indicate that the proposed method can effectively improve the resolution and visual effects of a power monitoring video, whose average value of the peak signal-to-noise ratio and structural similarity index reaches 27.08 and 0.819 respectively.