The study of cervical nucleus segmentation is of great significance for cervical cancer screening and diagnosis, but it brings great challenges to the segmentation task due to the influence of blurring edge and interference. In response to this problem, a nuclear segmentation method based on the DeepLabv3+network was proposed. Firstly, utilizing the output of the backbone network for multi-scale feature fusion and introducing an attention mechanism, a cell mass segmentation model was built to reduce the effects of interferences in the background on nuclear segmentation. Based on this, a two-path feature extraction module combining Transformer and ResNet50 was designed, which takes into account the sensitivity of the model to global information acquisition and low-level context features, and improves the discrimination ability of the model to nuclei and interference information. The experimental results show that the algorithm has achieved good segmentation results in the task of cervical cell nuclei, and MIoU is 0.832 9, which has increased by 2.33% and obtained better performance indicators compared to other methods.