To address the insufficient feature extraction of the FSRCNN model and its artificial redundant information caused by deconvolution, this study proposes an image super-resolution reconstruction algorithm based on a multi-scale fusion convolutional neural network. Specifically, a multi-scale fusion feature extraction channel is designed to cope with the insufficient utilization of image information of different sizes. Then, sub-pixel convolution is used for up-sampling in the image reconstruction part to suppress the artificial redundant information caused by deconvolution. Compared with the FSRCNN model, the algorithm in this study respectively increases the PSNR and SSIM by 0.14 dB and 0.001 0 on average at an amplification factor of 2 and by 0.48 dB and 0.009 1 on average at an amplification factor of 3 on Set5 and Set14 data sets. Experimental results show that the proposed algorithm can better retain the texture details of the image and improve the overall image reconstruction effect.