An image super-resolution network model is proposed based on a multi-scale recursive network herein. The proposed model mainly comprises a plurality of multi-scale feature mapping units, each of which includes a set of feature extraction layers with different scales, a fusion layer, and a mapping layer. The network performs feature extraction directly on an original low-resolution image, which is then reconstructed into a high-resolution image via sub-pixel convolution. In the training phase, the adaptive optimization method is used to accelerate the convergence of the network model. The experimental results show that the proposed algorithm achieves better super-resolution results, significantly improves the subjective visual effects, and sharpens the image texture. The objective evaluation indicators (PSNR and SSIM) of the proposed algorithm on the common test sets such as Set5, Set14, BSD100, and Urban100 are higher than those of the existing mainstream algorithms.