Aiming at the problems such as lack of contextual information connection， inaccurate and low-precision segmentation of cervical cell nucleus images， a cervical cell nucleus segmentation network named DGU-Net （Dense-Guided-UNet） was proposed on the basis of improved U-net combined with dense block and U-shaped convolutional multi-scale guided filtering module， which could segment cervical cell nucleus images more completely and accurately. Firstly， the U-net model with encoder and decoder structures was used as backbone of the network to extract image features. Secondly， the dense block module was introduced to connect the features between different layers， so as to realize transmission of contextual information， thereby enhancing feature extraction ability of the model. Meanwhile， the multi-scale guided filtering module was introduced after each downsampling and before each upsampling to introduce obvious edge detail information in the grayscale guided image for enhancement of the image details and edge information. Finally， a side output layer was added to each decoder path， so as to fuse and average all the output feature information， thereby fusing the feature information of different scales and levels to increase accuracy and completeness of the results. Experiments were conducted on Herlev dataset and the proposed network was compared with three deep learning models： U-net， Progressive Growing of U-net+ （PGU-net+）， and Lightweight Feature Attention Network （LFANet）. Results show that compared with PGU-net+， DGU-Net increases the accuracy by 70.06%； compared with LFANet， DGU-Net increases the Intersection-over-Union （IoU） by 6.75%. It can be seen that DGU-Net is more accurate in processing edge detail information， and outperforms the comparison models in segmentation indicators generally.