To solve the problems of insufficient utilization of residual features and loss of details in existing residual networks， a deep neural network model combining the two-layer structure of residual aggregation and dual-attention mechanism with receptive field expansion， was proposed for Single Image Super-Resolution （SISR） reconstruction. In this model， a two-layer nested network structure of residual aggregation was constructed through skip connections， to agglomerate and fuse hierarchically the residual information extracted by each layer of the network， thereby reducing the loss of residual information containing image details. Meanwhile， a multi-scale receptive field expansion module was designed to capture a larger range of context-dependent information at different scales for the effective extraction of deep residual features； and a space-channel dual attention mechanism was introduced to enhance the discriminative learning ability of the residual network， thus improving the quality of reconstructed images. Quantitative and qualitative assessments were performed on benchmark datasets Set5， Set14， B100 and Urban100 for comparison with the mainstream methods. The objective evaluation results indicate that the proposed method outperforms the comparative methods on all four datasets； compared with the classical SRCNN （Super-Resolution using Convolutional Neural Network） model and second best performing comparison model ISRN （Iterative Super-Resolution Network）， the proposed model improves the average values of Peak Signal-to-Noise Ratio （PSNR） by 1.91， 1.71， 1.61 dB and 0.06， 0.04， 0.04 dB， respectively， at the magnification of 2， 3 and 4. Visual effects show that the proposed model reconstructs clearer image details and textures.