For infrared and visible image fusion based on generation countermeasure networks, a deep fusion method based on adaptive feature enhancement and generator path interaction is proposed to solve problems, such as edge blurring between different objects in the fusion result, insufficient extraction of source image information, and imbalance of fusion information. First, the adaptive enhancement block sharpens the edge information of different objects in the source image according to the weight map. After the adaptive feature enhancement loss, intensity loss, and gradient loss are jointly constrained, the contrast and texture details of the fused image can be enhanced simultaneously. Second, the generator path interaction structure can fully extract the source image information by adding an interactive convolution layer between the two main paths, and the transmission of the feature map can be enhanced using a densely connected convolution network. In addition, a content loss function, designed based on the primary, secondary, and dual discriminator introduced in the network structure, ensures the balance between contrast and texture details in the fusion results. Experimental results show that the proposed method has very competitive results in both subjective visual and objective quantitative evaluations and is faster than the other methods.