In order to guarantee the reconstruction visual quality while improving the reconstruction rate, this paper proposes a video super-resolution reconstruction algorithm (SPyGAN) based on spatial pyramid generative adversarial network, which uses a more lightweight spatial pyramid network structure SPyNet and a more efficient upsampling method based on TecoGAN, and can quickly reconstruct the high-frequency texture details of images. In this paper, we mainly improve the optical flow prediction network, image reconstruction module and loss function part of the generative adversarial network TecoGAN. Experimental results show that the algorithm has improved the mean values of PSNR and SSIM compared with TecoGAN, in addition to the reduction of the parameter amount to 53.86%, and the reconstruction rate is improved to 239%, which effectively improves the reconstruction rate of the model.