Aiming at the issues of inadequate feature extraction and insufficient ability to reconstruct high-frequen- cy details in the information recovery process of image super-resolution reconstruction algorithm , a multi-scale fused image super-resolution reconstruction algorithm ( SRGAN-MCA) based on the attention mechanism is proposed on the basis of SRGAN. First , a multi-scale dense residual attention module based on coordinate attention mechanism is con- structed to extract feature information at different scales to solve the problem of inadequate feature extraction in the process of nonlinear mapping of image super-resolution reconstruction ; second , the Lipschitz constant of the discrimi- nator is constrained by embedding spectral normalization in the network discriminator to enhance the stability of net- work training; finally , the Charbonnier loss function to SRGAN-MCA for training optimization to achieve higher quality reconstruction. The experimental results on Set5 , Set14 , and BSD100 datasets show that the peak signal-to-noise rati- o ( PSNR) is improved by 0. 35 dB and 0. 47 dB on average , and the structural similarity ( SSIM) is improved by 0. 006 and 0. 016 on average for the 2 and 4 magnification reconstructed images compared with SRGAN.