Digital pathology images often suffer from a low resolution due to limitations in acquisition and storage, which severely affects diagnosis and machine analysis. To address the issue of low-resolution pulmonary H&E-stained sections, we propose a deep learning network model, the lung cytopathological images super-resolution and segmentation network (LCSr-SeNet), which aims to simultaneously enhance image resolution and achieve cell segmentation. LCSr-SeNet consists of two modules: a super-resolution module and a segmentation module. The super-resolution module, through progressive feature extraction and reconstruction, significantly enhances the details and clarity of pathology images. The segmentation module then precisely segments the enhanced high-resolution images, distinguishing cancer cells from normal cells. In quantitative evaluations, LCSr-SeNet demonstrates significant advantages in both image resolution enhancement and cell segmentation tasks. In terms of its peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), LCSr-SeNet significantly outperforms traditional methods. Additionally, in cell segmentation tasks, the model exhibits excellent performance in metrics such as the Dice coefficient and intersection over union (IoU), greatly improving the segmentation accuracy and robustness. LCSr-SeNet successfully overcomes the challenges posed by low-resolution sections, achieving precise segmentation and localization of cells in pulmonary tissues. This innovative method provides a new solution for pulmonary pathology image analysis and holds promise for significant contributions to the diagnosis and treatment of lung cancer.