Early diagnosis and prevention of colorectal cancer rely on colonoscopic polyp examination.Accurate automated polyp segmentation technology can assist clinicians in precisely identifying polyp regions, thereby conserving medical resources. Although deep learning-based image processing methods have shown immense potential in the field of automatic polyp segmentation, current automatic segmentation methods for colorectal polyps are still limited by factors such as the complex and variable intestinal environment and issues related to detection equipment like glare and motion blur. These limitations result in an inability to accurately distinguish polyps from surrounding mucosal tissue and effectively identify tiny polyps. To address these challenges, we designed a multi-attention-based model, PVT-MA. Specifically, we developed the Cascading Attention Fusion (CAF) Module to accurately identify and locate polyps, reducing false positives caused by environmental factors and glare. Additionally, we introduced the Series Channels Coordinate Attention (SCC) Module to maximize the capture of polyp edge information. Furthermore, we incorporated the Receptive Field Block (RFB) Module to enhance polyp features and filter image noise.We conducted quantitative and qualitative evaluations using six metrics across four challenging datasets. Our PVT-MA model achieved top performance on three datasets and ranked second on one. The model has only 26.39M parameters, a computational cost of 10.33 GFlops, and delivers inference at a high speed of 47.6 frames per second (FPS).