Super-resolution (SR) plays an important role in the transmission and display of video. To ensure the clarity of reconstructed video and real-time display,a fast vide o SR method based on succinct convolutional neural networks (CNN) is proposed.The proposed succin ct CNN consists of the following three points:First,Considering that the size of the input directly affects the network′s operating speed,the proposed network eliminates pre-interpolatio n process of the traditional method,directly extracts features from multiple low-resolution inp uts of the video frame and fuses multi-dimensional feature channels.Then,in order to avoid the loss of important information with zero gradient in the network,parametric rectified linear unit (PReLU) is used as the activation function,and the network structure is adjusted with a smaller fi lter size for multi-layer mapping.Finally,a deconvolution layer is added at the end of the network to obtain the reconstructed video.The experimental results show that compared with the re presentative methods,the proposed method has an average increase of 0.32dB and 0.016on the PSNR and SSIM.At the same time,an average reconstruction speed of 41frames per second is achieved under the GPU.The results show that the proposed method can quickly reconstruct better quality video.