Due to the diversity of the distribution and shape of rain streaks, existing rain removal algorithms produce problems such as blurred image background and poor generalization performance while removing rain. A multi-scale feature fusion image rain removal method based on an attention mechanism is proposed to address these problems. The feature extraction stage consists of multiple residual groups containing two multi-scale attention residual blocks. The multi-scale attention residual blocks use the multi-scale feature extraction module to extract and aggregate feature information at different scales and further improve the feature extraction capability of the network through coordinate attention. Local feature fusion is performed within groups, and the global feature fusion attention module is used between groups to better fuse features at different levels and to focus the network on rain streaks regions through pixel attention. The quantitative metrics of the proposed method are significantly improved compared with other existing image rain removal algorithms on both simulated and real rain image datasets, and the visual effects of the rain removal images are better and have good generalization.