A super-resolution computed tomography (CT) reconstruction method based on a residual attention aggregation dual regression network (RAADRNet) is proposed to improve the quality of CT image reconstruction. The multi-feature down-sampling extraction block (MFDEB) is used to complete multi-feature down-sampling extraction by employing average pooling, maximum pooling, and convolution operations, and channel learning attention (CLA) and spatial learning attention (SLA) are embedded after multi-feature fusion. Moreover, the shallow features of an image are extracted by combining the previous fusion features. CLA and SLA respectively introduce channel weight feature learning and activation function 1+tanh() to complete feature extraction. The residual attention aggregation block (RAAB) requires the use of the residual channel learning attention block (RCLAB) composed of a CLA-embedded residual network and the spatial feature fusion block (SFFB) composed of SLA for jointly extracting the deep features of the image. The primal network completes reconstruction after the feature fusion of shallow features and deep features amplified by sub-pixel convolution. The dual network further constrains the solution space of the reconstructed mapping function. Experiments show that the proposed algorithm improves the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the reconstructed image.