Explore the method of improving the U-net++ network and adding multi-channel feature fusion based on ultrasound images to achieve accurate and efficient automatic detection of intestinal endometriosis. The proposed neural network is improved on the segmentation network based on U-net++, adopting an end-to-end structure, inputting ultrasound image and its edge extraction image, and outputting the detection result of intestinal endometriosis.The experimental data were obtained from the endoscopic endoscopy images of 166 patients with intestinal endometriosis in Shenzhen People's Hospital. 133 cases were randomly selected as training samples and 33 test samples.In the network training process, the ten-fold cross-validation method is used for verification. The results show that On the 33 test set samples, the final average detection rate, precision rate, and recall rate of this method were 90.9%, 72.4%, and 89.8%, respectively. The improved neural network and multi-channel feature fusion input method can automatically detect the intestinal endometriosis area, and the detection has high robustness and accuracy, which can be used as a reference to assist doctors in clinical decision-making and intervention.