The image super-resolution algorithm adopts the same processing mode in channels and spatial domains with different importance, which leads to the failure of computing resources to concentrate on important features. Aiming at the above problem, an image super-resolution algorithm based on dynamic attention network was proposed. Firstly, the existing way of equalizing attention mechanisms was changed, and dynamic learning weights were assigned to different attention mechanisms by constructed dynamic attention modules, by which high-frequency information more needed by the network was obtained and high-quality pictures were reconstructed. Secondly, the double butterfly structure was constructed through feature reuse , which fully integrated the information from the two branches of attention and compensated for the missing feature information between the different attention mechanisms. Finally, model evaluation was conducted on Set5, Set14, BSD100, Urban100 and Manga109 datasets. Results show that the proposed algorithm has better overall performance than other mainstream super-resolution algorithms. When the amplification factor was 4, compared with the sub-optimal algorithm, the peak signal-to-noise ratio values were improved by 0.06, 0.07, 0.04, 0.15 and 0.15 dB, respectively, on the above five public test sets.