In order to achieve a good balance between infrared and visible image information, this paper proposes a deep multi-classification method of infrared and visible image fusion based on generative adversarial network technology. In this method, the idea of principal and auxiliary is introduced into the gradient and intensity information extraction of generator, and the depth and shallow network information extraction ability of generator convolution layer is improved. In the discriminator, multiple classifiers are used to estimate the distribution of visible and infrared regions simultaneously. After continuous face-off learning, the fusion results have remarkable contrast and rich texture details. The obtained information entropy and Shannon entropy value are 6.86, mutual information value is 13.72, standard deviation value is 34.82 and structural similarity value is 0.71.The experimental results show that the proposed method achieves better performance of infrared and visible image fusion in subjective and objective evaluation.