Convolutional neural network-based semantic segmentation models do not effectively explore feature weight information， which will result in under-segmentation of segmentation boundaries in complex scenes of computed tomography images. To address this problem， an improved U-Net++ model is proposed to explore adaptive weighted aggregation strategy based on U-Net++， and the improved U-Net++ model is applied to the segmentation of lung nodules in computed tomography images. In the convolutional neural network phase， the information from the different levels of deep features is extracted and combined with the weighted aggregation module， and thus the weights of features in each layer are adaptively learned. Then the learned weights are loaded on each feature layer and obtained a sampled segmentation map， and the final segmentation result can be obtained. Segmentation experiments are carried out on the lung cancer data sets of LIDC and Chongqing University Cancer Hospital. The intersection over union of the proposed improved U-Net++ method on two datasets reach 80.59% and 87.40%， and the DICE of this method on two datasets could reach 88.23% and 90.83%， respectively. Compared with U-Net and U-Net++， the proposed algorithm significantly improves the segmentation performance of lung nodules in computed tomography images. The experimental results show that improved U-Net++ achieves accurate segmentation on tiny details of tumors， and it bring beneifits to solve the problem of under segmentation when lung nodules grow invasively to the surrounding.