Accurate segmentation of polyps in colonoscopy images has become a key aspect in the diagnosis of colon cancer.A colon polyp segmentation algorithm with improved cascade U-Net structure is proposed to address the problems of holes,rough segmentation and incomplete segmentation in the current colon polyp segmentation algorithm.Using the idea of feature fusion,a multi-scale semantic embedding module and a residual module are designed to make full use of the semantic information of deep and shallow features.An attention mechanism is introduced and an improved null convolution module is built at the cascade of the model to expand the convolutional field of perception and enhance feature capture.The convolutional layer module and segmentation loss function are improved to promote the generalization and robustness of the model.The experimental analysis on the Kvasir-SEG dataset achieves 90.39%,88.34%,83.62% and 95.12% for similarity coefficient,average intersection ratio, recall and accuracy, respectively. The experimental results show that the proposed algorithm improves the problems of internal holes,rough edges and incomplete segmentation of segmented images and outperforms other polyp segmentation algorithms.