To solve the poor image quality and subsequent low efficiency of machine vision tasks on rainy days,an image rain removal algorithm based on multi-feature interaction and dense residual is proposed.First,a multi-feature interactive convolution module is proposed to extract the semantic features of rain streaks in different spaces to enhance information utilization.Second,a multidimensional space weight attention module is constructed,and the weights of different spatial information are preliminarily integrated to enhance the characteristics of rain streaks.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 corrects the rain information.Finally,the output image quality is improved through the linear combination of various loss functions as well as the rainy day imaging model.Experiments on several public datasets show that the subjective and objective evaluation indexes of the proposed algorithm outperform those of the classical algorithm and novel algorithms,and the detailed background information of the images can be better preserved while removing the rain streaks.