The existing super-resolution reconstruction algorithms of single image mostly pursue the peak signal-to-noise ratio （PSNR）， and lack the attention to the details of image texture in the process of feature extraction， resulting in poor subjective perception of reconstructed images. In order to solve this problem， this paper proposes a single image super-resolution reconstruction algorithm based on convolutional neural network gradient and texture compensation. Specifically， three branches are designed for structure feature extraction， texture detail feature extraction and gradient compensation， and then the proposed fusion module is used to fuse the structure feature and texture detail feature. To prevent the loss of texture information in the reconstruction process， this paper proposes a texture detail feature extraction module to compensate the texture detail information of the image and enhance the texture retention ability of the network. At the same time， this paper uses the gradient information extracted by the gradient compensation module to enhance the structure information. In addition， this paper also constructs a deep feature extraction structure， combining channel attention and spatial attention to screen and enhance the information in the deep features. Finally， the second-order residual block is used to fuse the structure and texture features， so that the feature information of the reconstructed image is more perfect. The effectiveness and superiority of the proposed method are verified by comparative experiments.