Aiming at the problem of image noise and artifact interference caused by the low-dose scanning method in Computed Tomography ( CT ), especially the large difference in noise and artifact intensity in different parts, a convolutional neural network algorithm based on dynamic controllable residual (DC-ResNet) was proposed. The main idea of DC-ResNet is to add an image quality-guided control variable to the regular residual network connection to allow a weighted sum of residuals to be obtained, thereby achieving dynamic controllability of residual features. The designed DCResNet network was a combined structure including two sub-networks: one was the basic sub-network as the backbone network, in which the global dynamic residual block and the local dynamic residual block were used to achieve the improvement of low-dose CT image quality; the other was a conditional sub-network, as an auxiliary network, which was used to generate the weights of different dynamically controllable residual blocks in the base subnet and assist the learning of the base subnet. Through the experimental verification of Mayo and UIH data, the visual results showed that the noise and artifacts of the processed CT images in different parts were well suppressed, and the structural details and tissue texture were effectively preserved. The quantification results showed that the peak-signal to noise ratio ( PSNR) and structural similarity (SSIM) of the processed CT images were better than those of the comparison methods.