Infrared and visible image fusion technology makes full use of the advantages of different sensors, retains the complementary information and redundant information of the original image in the fused image, and improves the image quality. In recent years, with the development of deep learning methods, many researchers have begun to introduce this method into the field of image fusion, and have achieved fruitful results. According to different fusion frameworks, the infrared and visible image fusion methods based on deep learning are classified, analyzed and summarized, the commonly used evaluation indicators and data sets are reviewed. In addition, some representative algorithm models of different categories are selected to fuse different scene images, the advantages and disadvantages of each algorithm are compared and analyzed by evaluation indicators. Finally, the research direction of infrared and visible image fusion technology based on deep learning is prospected, infrared and visible fusion technology is summarized, which is the basis for future research work.