In order to solve the problem that the poor image quality leads to the low efficiency of subsequent machine vision tasks in rainy days, an image rain removal algorithm based on multi-feature interaction and dense residual is proposed. Firstly, a multi-feature interactive convolution module is proposed to extract the semantic features of rain lines in different spaces in order to improve the utilization of information. Secondly, a multi-dimensional spatial weight concern module is constructed, and the weights of different spatial information are preliminarily integrated to enhance the characteristics of rain lines. Then combining the advantages of dense connection and residual network, a dense residual fusion module is designed, which improves the learning ability of the network, realizes the reuse of information, and further correct the rain information. Finally, through the linear combination of various loss functions and rainy day imaging model, the output image quality is improved. Experimental results on several public data sets show that the subjective and objective evaluation indexes of the algorithm are better than those of the classical algorithm and novel algorithms, and the detailed information of the image background can be preserved more effectively while removing the rain pattern, which lays a foundation for the effective development of subsequent tasks based on machine vision.