In view of the increasingly higher requirements for the definition of video images in the fields of surveillance cameras, satellite remote sensing, and video entertainment, most of the current video super-resolution methods have problems such as large parameters and jitter in the restored video. A video super-resolution method combining multiple attention and optical flow is proposed. By introducing multiple attentions including spatial attention, channel attention and self-attention, the super-resolution performance is improved. Specifically, as a feature-weighted enhancement method, these attentions can capture the spatio-temporal features of video frames and enhance the adaptability and inter-channel dependence to achieve the function of global learning. At the same time, the idea of two-stage feature alignment is proposed. First, the optical flow is used to estimate the video, the first stage of feature alignment is performed, the introduction of length is a feature fusion of memory network structures that enhance locations and channels, and the second stage of feature alignment is performed to prevent the recovered video frame from shaking. The experimental results show that the method has satisfactory results in both the evaluation standard and the visualization effect.