To solve the problem of serious degradation of images captured by imaging equipment in hazy scenes, a single image dehazing algorithm based on feature constraint cycle generative adversarial networks (CycleGAN) is proposed. First, the mapping relationship between the hazy image and the clear image is learned by the CycleGAN, and the discriminator determines whether the reconstructed image conforms to the data distribution of the real sample image. Then, as for network model loss function, the joint function based on cyclic consistent loss and Haze loss is constructed. The frequency information of image is introduced into Haze loss function as a constraint term and the content loss of high-dimensional feature is extracted by the pre-trained VGG-16 model, which can improve dehazing performance of the network and solve the problems of insufficient dehazing and image information loss in CycleGAN model. Finally, the peak signal-to-noise ratio and structural similarity index are used as the evaluation criteria for quantitative analysis between the input hazy image and the output dehazing enhanced image. Experimental results show that the proposed algorithm can effectively reduce the effect of haze on imaging quality, and obtain better subjective visual evaluation and objective quantitative evaluation.