Aiming at the problems of insufficient feature extraction， inefficient utilization， and insufficient ability to reconstruct high-frequency details in the process of image high-frequency information restoration， an image super-resolution reconstruction algorithm based on information distillation cascade telescopic network is proposed in this paper. Firstly， a feature scalable information distillation block is constructed， which solves the problem of insufficient feature extraction in the nonlinear mapping process of image super-resolution reconstruction of information distillation by expanding the feature receptive field of input information and using channel attention to extract interested information. Then， a cascaded residual superposition mapping block is designed， which combines multiple residual blocks. By leading out the residual part in the residual structure and cascading superposition， the transmission of information between information distillation blocks is increased， so that the extracted feature information contains more details. Experimental results show that the reconstructed image of this algorithm is clearer than that of other comparison algorithms， and the peak signal-to-noise ratio （PSNR） and structural similarity （SSIM） are greatly improved．