In order to solve the problems of poor segmentation effect caused by th e pose change, large size difference and edge detail loss of the existing body image foreground target,a body foreground segmentation algorithm based on deformable effective receptive field is proposed.The algorithm merges feature maps of different scales to reduce the spatial semantic information lost in the downsampling process;the variable effective receptive field module and t he edge refinement module are combined to capture spatial information and semantic information to i ncrease the effective receptive field range of the algorithm for different targets,and make the effective receptive field expand with the shape change of target attitude,size and so on;finally, focal loss is used to alleviate the imbalance between positive and negative samples.The experimental results show that, on the baidu people segmentation dataset,compared with other mainstream semanti c segmentation algorithms,the intersection ratio of the algorithm is as high as 88.45%,1.07% higher than the mainstream semantic segmentation algorithm deeplab V3+,3.71% higher than the c lassic algorithm U-net,and it has fast running speed,good stability,high timeliness and good robustness.