Accurate segmentation of lung parenchyma plays a crucial role in computer-aided diagnosis systems for pulmonary carcinoma. While existing deep learning-based segmentation architectures demonstrate competent performance in processing large and well-defined lung regions, they exhibit notable limitations in capturing small and indistinct parenchymal areas. To address this critical challenge, we propose a novel Multi-level Feature Enhancement Network (MFE-Net) architecture based on U-Net framework. Firstly, we develop a Multi-Receptive Field Fusion Module (MRFB) to replace conventional convolutional blocks in the encoder pathway. This hierarchical architecture enables simultaneous extraction of multi-scale contextual information through parallel dilated convolution branches with varying dilation rates. Secondly, we introduce a Mixed Local Channel Attention (MLCA) mechanism within skip connections to establish cross-level feature interactions. Thirdly, we formulate a hybrid loss function combining Binary Cross-Entropy (BCE) and Dice loss to optimize both pixel-wise classification accuracy and regional shape consistency, particularly beneficial for segmenting marginal regions and small parenchymal lesions.In order to verify the effectiveness of the method, we conducted extensive experiments on the LIDC-IDRI lung CT dataset, and the experimental results showed that the overall average Dice Similarity Coefficient (D-S) of this network reached 93.58%. The proposed architecture demonstrates dual advantages in pulmonary tissue segmentation: it significantly enhances delineation accuracy for subtle, low-contrast parenchymal regions while preserving precise segmentation performance for well-defined macroscopic structures.