Aiming at the problems in existing infrared and visible image fusion methods, such as the difficulty in fully extracting and preserving the source image details, contrast, and blurred texture details, this paper proposes an infrared and visible image fusion method guided by cross-domain interactive attention and contrastive learning. First, a dual-branch skip connection detail enhancement network was designed to separately extract and enhance detail information from infrared and visible images, using skip connections to prevent information loss and generate enhanced detail images. Next, a fusion network combining a dual-branch encoder and cross-domain interactive attention module was constructed to ensure sufficient feature interaction during fusion, and the decoder was used to reconstruct the final fused image. Then, a contrastive learning network was introduced, performing shallow and deep attribute and content contrastive learning from the contrastive learning block, optimizing feature representation, and further improving the performance of the fusion network. Finally, to constrain network training and retain the inherent features of the source images, a contrast-based loss function was designed to assist in preserving source image information during fusion. The proposed method is qualitatively and quantitatively compared with current state-of-the-art fusion methods. Experimental results show that the eight objective evaluation metrics of the proposed method significantly outperform the comparison methods on the TNO, MSRS, and RoadSence datasets. The fused images produced by the proposed method have rich detail textures, enhanced sharpness, and contrast, effectively improving target recognition and environmental perception in real-world applications such as road traffic and security surveillance.