Endoscopic biopsy is the main approach to the clinical diagnosis of early gastrointestinal cancer to date. However， this approach warrants a long period to obtain the final diagnosis. Endocytoscopy is a type of endoscope with ultra-high magnification， which， combined with intraoperative staining， can directly observe the pathological structure of the lesion such as the nucleus in vivo. To make endoscopists more accurately analyze the pathological features of the nucleus during the operation， a nuclear staining and segmentation method was previously developed for the esophageal mucosa tissue of pigs based on the endocytoscopy system with high magnification. Firstly， 1% toluidine blue was used to stain the nucleus of esophageal mucosa tissue， and the stained nuclei were observed successfully under the microscopic imaging mode of endocytoscopy. Based on this， the deep learning method was adopted to train the nuclear segmentation model， which effectively realized the segmentation and extraction of stained nuclei. The pixel accuracy reaches 99.23%， specificity of 99.54%， sensitivity of 84.37%， and the Dice of 0.813 8， laying a foundation for the study of artificial intelligence-assisted diagnosis of endocytoscopy.