Because of COVID- 19's highly infectious , early diagnosis and treatment are the key factors to reduce the losses caused by the epidemic. In order to assist doctors in the diagnosis of COVID- 19 and efficiently segment CO- VID- 19 lesions from lung CT slices , an improved encoder-decoder deep neural network based on the U-Net with ex- cellent image segmentation effect , Multi-scale Attention Network ( MSANet ) is proposed. By using a global pooling layer and setting a sampling rate for void convolution , the network receptive field is increased , and multiscale informa- tion is captured to achieve effective segmentation of large objects. MSANet uses channel attention and spatial attention to model in the spatial dimension to effectively extract deep image features. The test results show that compared with U -Net network , the improved algorithm improves the mean intersection over union of segmentation by 1. 46% , improves the mean pixel accuracy of category by 0. 8% , and improves the accuracy by 1. 17% .