An image super-resolution reconstruction method based on multi-scale feature mapping network was proposed for the problems of shallow network, low utilization rate of features and fuzzy reconstructed images, which existed in the super-resolution convolutional neural network (SRCNN). Multi-scale low-resolution (LR) features were mapped into high-resolution (HR) feature space by learning the mapping relation between LR features and HR features, and the utilization rate of features in the reconstruction process was improved by using feature concatenation. A joint loss function consisting of the pixel-wise loss, the perceptual loss and the adversarial loss was defined, which performed well in restoring the low-frequency content, the sharp edges and the high-frequency textures of the reconstructed images. The experimental results of datasets Set5, Set14 and BSD100 for the upscaling factor 4 were compared with those of state-of-the-art methods. The proposed method performs well in improving the perceptual quality of the reconstructed images in order to achieve clearer edges and textures, and has better scores in the objective evaluation.