As insulators play an important role in power transmission lines, we propose an intelligent method based on the convolution neural network (CNN) to evaluate the corona discharge of an insulator. In this method, the imaging device is a dual-spectra camera with a visible channel and an ultraviolet (UV) channel. The CNN is adopted to identify the detection distance of the insulator with the visible channel. To train the network, the dataset of the insulator is obtained by the experimental setup and deep convolutional generative adversarial networks. Through adjusting the training parameters and optimizing the network structure, an optimal trained model is achieved. Then the image pixel ratio method is adopted to measure the UV signal strength of the images captured by the UV channel. Meanwhile, the relationship between the detection distance and the UV signal strength is discussed. The critical value for the corona discharge of the insulator is obtained via experiments at the standard detection distance. Finally, the corona discharge of the insulator is evaluated by combining the detection distance with the UV signal strength. The experimental results show the method has the advantages of high accuracy and robustness and can effectively evaluate the corona discharge of the insulator.