Accurate image segmentation of pulmonary nodules is of great significance for the early diagnosis of lung cancer. To solve the problem of insufficient feature extraction and detail loss caused by multiple scales and blurred edges of pulmonary nodules image, this study proposes a pulmonary nodule image segmentation network named RAVR-UNet, which incorporates multi-scale features and double-branch parallel. Firstly, given the inability of the U-Net network to fully extract pulmonary nodule features in the coding stage, a double-branch parallel feature aggregation network is used to extract the feature information from pulmonary nodule images to reduce the information loss during feature coding. Secondly, the Agent_ViT module is introduced to enhance the capability of global information modeling while maintaining linear computation. Then, to recover the lost pulmonary nodule spatial information during subsampling, a multi-scale feature fusion module is added in the decoding stage. Finally, a mixed loss function is designed to alleviate the imbalance between positive and negative samples in the pulmonary nodule image segmentation task. Experimental results on the LIDC-IDRI public dataset show that the similarity coefficient and intersection over union (IoU) of the proposed network reach 93.15% and 87.63%, respectively, which is better than the mainstream pulmonary nodal segmentation algorithms, and the segmentation results are closer to the real values.