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As a relatively common natural weather condition, rainy days will significantly affect the imaging quality of images and video data captured by outdoor vision systems and restrict the performance of subsequent advanced computer vision tasks. In order to fully extract image features, effectively remove rain streaks, and improve the efficiency of rain removal, a novel single-stage deep learning rain removal method is proposed to solve the problems of artifacts and loss of details in current rain removal algorithms. The combination of efficient convolution and cross-scale self-attention was used to make up for the global feature modeling capabilities that pure convolutional networks cannot meet. Embedding multi-scale spatial feature fusion modules to increase the receptive field of the network effectively and enhance the network's ability to learn rain streak features of different distributions. A hybrid loss function was designed to use the advantages of each loss function to make up for the shortcomings of a single loss function. A large number of experiments on different types of datasets have proved that the algorithm can effectively remove rain streaks, fully retain background details, and has a significant increase in processing speed.