The invention belongs to the technical field of image super-resolution reconstruction, and discloses a multi-scale residual attention network image super-resolution reconstruction method based on attention. The method comprises the steps of selecting a common image data set as a to-be-experimented image set, dividing the to-be-experimented image set into an image training set and an image test set, and performing image preprocessing, designing a multi-scale residual structure unit module, introducing a channel attention mechanism, and building a multi-scale residual attention neural network model based on channel attention, inputting the preprocessed image training set into a multi-scale residual attention neural network model based on channel attention for model training, and inputting the preprocessed image test set into the trained model for testing to obtain a finally reconstructed high-resolution image. According to the method, a basic unit is enabled to focus on extraction of high-frequency information, important feature map information in a channel is better highlighted, important information in an image is better extracted, and reconstruction errors are reduced.