In order to solve the problems that the current deep learning dehazing algorithm being ineffective in using multi-scale features when processing non-uniform foggy images, resulting in color distortion and incomplete detail recovery of the restored images, this paper proposes an image dehazing algorithm with mixed attention and multi-feature interaction. Firstly, the coding module is used to extract features at different scales. Secondly, a mixed attention module is constructed to globallay perceive the image fog weights to different fog concentrations using channel attention mechanisms. Then, a multi-feature interaction module is designed to facilitate information exchange between features of different scales, effectively use the semantic information in low-resolution features, retain the spatial details and color information of high-resolution features, and use the gated fusion module to aggregate features of different scales. Finally, the decoding module reconstructs the fused features to obtain a fog-free image. Experimental results show that the dehazing images recovered by the proposed algorithm not only have natural colors and clear details subjectively, but also outperform the existing mainstream algorithms in objective indicators. These research findings offer novel approach for both research and application of deep learning-based image dehazing.