At present,there exists too many parameters amount in the real-world image super-resolution (SR) reconstruction algorithm based on deep learning.To solve this problem,a lightweight real-world image SR reconstruction algorithm based on multi-scale residual feature aggregation is proposed.First,the depthwise separable convolution and multiplexing convolution are used to improve the existing multi-scale feature extraction block,which achieves the lightweight of the module while extracting the extra-multi-scale feature,with only 7.5% of the parameters amount before the improvement.Next,the residual feature aggregation is exploited to aggregate the 4 multi-scale depthwise separable blocks amount (MSDSB) into a residual feature aggregation block to reduce the length of the residual path.Then,the enhanced attention module is utilized to adaptively adjust the channel and spatial dimensions to improve the performance of the algorithm.Finally,the adaptive upsampling module is used to obtain SR reconstructed images.In ablation experiments,the reconstruction performance of the algorithm is better than that of the original algorithm,and the parameters amount is only 3.53×10⁶,which is 34.5% of the original algorithm.In the comparative experiments,the reconstruction performance of the proposed algorithm is better than the current mainstream algorithm. Compared with the component divide-and-conquer (CDC) algorithm,the PSNR and SSIM indexes of the presented algorithm are increased by 0.01dB and 0.001 0,respectively,and the parameters amount is only 8.84% of that of the CDC algorithm. The lightweight of the algorithm is realized while ensuring the reconstruction performance.