This paper proposes a multi-branch feature cascade image deraining network based on the attention mechanism to address the problems that existing deraining networks do not entirely deraining in diverse environments and do not adequately preserve image texture details.The model combines multiple attention mechanisms to form multi-branch networks to transfer and cascade the spatial image details and contextual feature information in the overall network and fuse them.Moreover,the stage attention fusion mechanism constructed between network branches can reduce the loss of image information during feature extraction and retain feature information to a greater extent,making the image deraining task more effective.The experimental results demonstrate that the new algorithm outperforms other comparison algorithms in terms of objective evaluation indices,the subjective visual effect can be effectively enhanced,the deraining ability is more substantial,the accuracy is more remarkable,and it can remove various densities of rain patterns while preserving the image's detail information.