Abstract: A video super-resolution method based on multi-scale characteristics residual learning convolutional neural network is pro-posed in this paper. Considering the spatial-temporal relationships between frames of video, five adjacent frames of video is preprocessed by bicubic interpolation as the input of the proposed convolutional neural network; After the network, the re-constructed intermediate frame is output, reconstructed in order till the whole high-resolution video is obtained. The pro-posed convolutional neural network is composed of four parts, including multi-scale feature extraction, residual learning, sub-pixel convolutional layer, and skip-connection. With multi-scale characteristics extraction and the residual learning, the proposed method provides abundant features and restoring more high-frequency information. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used as the loss function to optimize the network. Experimental results have shown that compared with latest algorithms, the proposed method has a significant improvement on the average evalu-ation indicator (PSNR +3.151dB, SSIM +0.102) and effectively reduces the edge blurring phenomenon of the subjective visual effect.