Low-dose CT is an effective and relatively safe screening method for thoracic and abdominal diseases, but the artifacts and noise in the image will seriously affect the doctor’s diagnosis. Network training in image enhancement methods based on deep learning mostly relies on paired data that is pixel-level matched low-dose and conventional-dose CT images at the same site of the same patient. An improved low-dose CT image enhancement network based on the cycle-consistent generative adversarial network (CycleGAN) was proposed for unpaired data. A shallow feature pre-extraction module was added in front of the generator to enhance the capability to extract CT images features. In addition, the depthwise separable convolution was used to replace some common convolutions in the generator to decrease network parameters and reduce GPU memory usage. In the proposed network, a total of 3 275 two-dimensional low-dose CT slices and a total of 2 790 two-dimensional unpaired conventional-dose CT slices were used for training, and a total of 1 716 two-dimensional low-dose CT slices were employed for testing. The results show that the averaged perception-based image quality evaluator (PIQE) of CT images generated by the network is 45.53, which is 8.3% lower than that of CycleGAN, 31.9% lower than that of Block-Matching and 3D filtering (BM3D), and 20.9% lower than that of unsupervised image translation network (UNIT). Moreover, the proposed network can produce better subjective visual effects in terms of structural detail preservation, noise and artifact suppression. This shows that the network is a low-dose CT image enhancement method with potential clinical prospects.