Aiming at the problems of low utilization of feature information and insufficient generalization ability in the traditional medical image segmentation network with encoding and decoding structure,this paper proposes a multi-scale semantic perceptual attention network (MSPA-Net) combined with encoding and decoding mode.Firstly,the network adds a dual-channel multi-information domain attention module (DMDA) to the decoding path to improve the ability of feature information extraction.Secondly,the network adds a dense atrous convolution module (DAC) at the cascade to expand the convolution receptive field.Finally,based on the idea of feature fusion,an adjustable multi-scale features fusion module (AMFF) and a dual self-learning recycle connection module (DCM) are designed to improve the generalization and robustness of the network.To verify the effectiveness of the network,the experimental verification is carried out on CVC-ClinicDB,ETIS-LaribPolypDB,COVID-19 CHEST X-RAY,Kaggle_3m,ISIC2017,and Fluorescent Neuronal Cells datasets,and the similarity coefficients reach 94.96%, 92.40%,99.02%,90.55%,92.32% and 75.32% respectively.Therefore,the new segmentation network shows better generalization ability,the overall performance is better than the existing network,and can better achieve the effective segmentation of general medical images.