Objective To address the issue that the diversity of polyp shapes and sizes in colon polyp images and the low contrast between polyps and their surrounding tissues affect the precise segmentation of polyps a colon polyp image segmentation method based on the DeepLabv3+ network model that integrates attention mechanisms and multilevel features is proposed. Methods Firstly the BAM_ASPP module was proposed and the BAM attention mechanism was added to the atrous spatial pyramid pooling ASPP module so as to obtain important semantic information and improve the feature representation ability of the model. Secondly after the feature fusion of the atrous spatial pyramid pooling module the shuffle attention SA mechanism was introduced to obtain the key features. Finally the convolutional block attention module feature fusion CBAMFF module was proposed which fused the features of the third and fourth layers of the backbone network. Meanwhile the attention mechanism was introduced to focus on location information to improve the segmentation accuracy of the model. Results Experimental results demonstrated that the mIoU and mDice of the proposed algorithm reached 91. 63% and 95. 54% respectively on the Kvasir-SEG dataset. Conclusion The segmentation performance of the proposed algorithm is superior to that of other compared methods. The proposed algorithm enables precise segmentation of colon polyp images thereby assisting doctors in accurately removing polyps.