To solve the challenges associated with the inadequate separation of source image features, low interpretability, and difficulty of designing accurate fusion rules, this paper proposes an infrared (IR) and visible image fusion method based on mutual information feature separation and representation, which effectively separates features while preserving the typical information of the source image. First, a mutual information constrained coding network is used to extract the features, maximize the mutual information between the source image and features to retain the feature representation of the source image, and minimize the mutual information of private and public features to achieve separation and representation. In addition, the loss function adopts a soft weighted intensity loss to balance the distribution of IR and visible features. Objective and subjective evaluation results of comparison experiments indicate that the proposed method can effectively fuse important information regarding IR and visible images and has good visual perception.