This paper presents a segmentation network that utilizes global feature guidance and attention to enhance the precision of cell segmentation, addressing the variability in size and shape of normal and abnormal nuclei in cervical cells as well as interference in cell images. Firstly, the U-shaped network structure is utilized as the main framework, with the introduction of a global feature guide module to comprehensively extract features at each stage for obtaining global context information at different levels. This overcomes the limitation of insufficient extraction ability of single-stage context information in U-shaped networks, enabling better handling of nuclei with different shapes and improving edge segmentation accuracy. Secondly, an improved attention-gate structure is incorporated to suppress interfering information in images, emphasize nucleus information, and enhance model discrimination against interfering data. Experimental results on the Herlev dataset demonstrate that our proposed method effectively enhances nuclear segmentation precision, achieving a Dice coefficient of 0.941 3 in quantitative analysis which presents certain advantages compared to other methods.