To address the problems of accurate segmentation difficulties for kidney structures caused by large differences between different structures， small sizes and thin structures of arteries and veins， and uneven grayscale distribution and artifacts in Computed Tomography Angiography （CTA） images， a kidney 3D structure segmentation model MDAUnet （MultiDecoder-Attention-Unet） based on multi-decoder and attention mechanism with CTA was proposed. Firstly， to address the problem that the network cannot share weights due to large differences between different structures， a multi-decoder structure was used to match different decoder branches for feature structures with different semantic structures. Secondly， to address the problem that it is difficult to segment blood vessels with small size and thin structure， an asymmetric spatial channel joint attention module was introduced to make the model more focused on tubular structures， and the learned feature information was simultaneously calibrated in spatial dimension and channel dimension. Finally， in order to ensure that the model paid enough attention to the vessel structure in back propagation， an improved WHRA （Weighted Hard Region Adaptation） loss was proposed as a loss function to dynamically maintain the inter-class balance of the vessel structure during training as well as to preserve the characteristics of the background information. In addition， in order to improve the contrast of the grayscale values of the feature map， the edge detection operator in traditional image processing was embedded into the pre-processing stage of the model， and the feature enhancement of the boundary of the region of interest to be segmented made the model more focused on the boundary information and suppressed the artifact information. The experimental results show that the Dice Similarity Coefficient （DSC）， Hausdorff Distance 95 （HD95） and Average Surface Distance （AVD） of the proposed MDAUnet model on the kidney structure segmentation task are 89.1%， 1.76 mm and 1.04 mm， respectively. Compared with suboptimal MGANet （Meta Greyscale Adaptive Network）， MDAUnet improves the DSC index by 1.2 percentage points； compared with suboptimal UNETR （UNEt TRansformers）， MDAUnet reduces HD95 and ASD indexes by 0.87 mm and 0.45 mm， respectively. It can be seen that MDAUnet can effectively improve the segmentation accuracy of the three-dimensional structure of the kidney， and help doctors to evaluate the condition objectively and effectively in clinical operations.