Abstract: Before lung cancer forms a tumor, it often appears in the form of lung nodules. Therefore, a correct diagnosis of lung nodules in time is of great significance to improve the survival rate of patients. This paper proposes a segmentation method of pulmonary nodules in CT images based on MA_MultiResUnet to assist doctors in diagnosing lung nodules. This method further obtained the feature map with multi-scale spatial information and prominent important channel features by redefining the skip connection structure in the model, and the channel attention module was introduced into the decoder to perform feature calibration, so as to improve the network's segmentation performance of lung nodules. The dataset used the LIDC-IDRI public dataset, and the proposed method was evaluated on the pre-processed dataset. The experimental results show that the Recall, Dice and Mlol performance of MA_MultiResUnet reach 85.76%、84.24%、86.99%, respectively, and the segmentation performance is better than the existing ones.