The VDSR (very deep super resolution) model has some problems such as neglecting the interconnection between feature channels, inability to fully utilize the features of each layer, excessive parameter quantity, and computational complexity. To solve these problems, this paper proposes a network structure based on a residual channel attention mechanism and multilevel feature fusion. By introducing residual channel attention, the channel's characteristic response is adaptively corrected to improve network representation ability. A recursive structure is adopted in the network and parameter sharing is implemented in each recursive block, which reduces the number of parameters. The proposed multilevel feature fusion method can fully extract image features; traditional convolution is replaced by group convolution to further reduce the number of parameters and computational complexity. The algorithm reduces the number of parameters and complexity of the model while ensuring the quality of image reconstruction. When an image is enlarged four times, parameter quantity and computational complexity are approximately 0.33 and 0.02 times, respectively, those of VDSR.