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Pulmonary vein anatomical structure typing plays a crucial role in the preoperative assessment and postoperative evaluation of lung tumor resection, atrial fibrillation radio frequency ablation, and other medical procedures. The accuracy of such typing relies heavily on the segmentation results of the left atrium and proximal pulmonary veins. However, due to the similarities in intensity between the left atrium, proximal pulmonary veins, and adjacent tissues in CT images, segmentation errors often occur, leading to subsequent inaccuracies in pulmonary vein classification. To address this issue, we propose an attention module called Dimensional Decomposition Attention (DDA), which combines Dimensional Decomposition Spatial Attention (DDSA) and Dimensional Decomposition Channel Attention (DDCA). DDA effectively leverages the spatial and channel information of 3D images to enhance the segmentation accuracy of the left atrium and proximal pulmonary veins. In DDSA, the input features are decomposed into three one‐dimensional directional features (height, width, and depth) and fused to generate weights that emphasize spatial shape features and focus on the region of interest. On the other hand, DDCA encodes the input features into dimensional channel features, fuses them with one‐dimensional directional features, and utilizes position encoding to reinforce the channel features and prioritize channels with relevant information. The performance of DDA was evaluated using a two‐stage experimental approach on datasets provided by The People's Hospital of Liaoning Province and the MM‐WHS CT dataset, yielding average Dice values of 93.93% and 90.80%, respectively, demonstrating the effectiveness of DDA. [ABSTRACT FROM AUTHOR]