Automatic and accurate polyp localization and segmentation methods can detect polyps in a timely manner in the early stage of colorectal cancer lesions， greatly reducing the risk of cancer transformation. The encoder-decoder architecture， as the most mainstream network structure in polyp segmentation in recent years， has been greatly improved， such as improving the model’s ability to capture global contextual and local features， and using deep features to guide shallow decoding. However， polyps vary in shape and size， and due to their convolutional nature， they are prone to getting too caught up in local information mining and losing remote information dependencies during encoding. Some polyp images also have low contrast and complex spatial characteristics， which makes it easy to confuse the polyp with the background. Based on this， this paper proposes a polyp segmentation network based on multiple attention and schatten-p norm（MASNet）. Among them， the axial multiple attention module utilizes axial attention to supplement remote contextual relationships in the image， while also paying attention to boundary and background information to achieve feature complementarity. It enhances the capture of local detail features while paying attention to global features. By utilizing the correlation between matrix singular values and matrix implicit information， the schatten-p norm is introduced as a constraint to analyze the data from a matrix perspective and assist the model in distinguishing foreground and background. By setting up a large number of experiments， the effectiveness of the proposed method is proven， and MASNet achieves the best segmentation results by comparing different advanced methods on the Kvasir-SEG dataset.