The invention provides an image super-resolution reconstruction method based on a fused attention mechanism residual network, and solves the problems of poor image reconstruction quality and non-idealvisual effect in the prior art. The researched image reconstruction method comprises the steps of S1, performing data acquisition and preprocessing, and obtaining a training image data set and a to-be-reconstructed image data set; S2, building a network model, wherein the model structure comprises a feature extraction layer, a feature learning layer and an image reconstruction layer; S3, initializing, training and storing model parameters to obtain an optimal model structure and an optimal parameter set; and S4, performing image super-resolution reconstruction, inputting a to-be-reconstructedimage, and outputting a high-resolution image under a corresponding amplification scale. The image super-resolution network provided by the invention combines global and local residual structures, fuses channel attention and spatial attention mechanisms, pays more attention to high-frequency information of the image, reserves important features in the image to the greatest extent, reduces repeated and redundant features, and greatly improves the details and definition of the reconstructed image.