Owning to the complex morphological structure of coronary artery and the variations in acquisition conditions of Computed Tomography （CT） Angiography （CTA） images， image quality issues such as uneven distribution of image gray scale， motion artifacts and noise， result in missed judgements and misjudgement problems in segmentation of coronary artery structure. Therefore， a segmentation network of coronary artery structure from CTA images based on multi-scale spatial features — Three-Dimensional （3D） Multi-Scale Parallel Net （MSP-Net） was proposed. Firstly， in view of characteristics of large spatial span and small local proportion of coronary artery， a multi-scale parallel fusion network was used to extract global features and local features from coronary artery CTA images respectively for fusion to ensure the complete extraction of coronary artery structure features. Secondly， by adopting a coarse to fine idea in coronary artery reconstruction to enhance redundancy of the image features， thereby ensuring clear boundaries of coronary artery， and then the coronary artery structure was reconstructed using the fusion method of different scale features to enhance accuracy of the segmentation results， thereby reducing missed judgements and misjudgments. Finally， in order to accelerate training process of the network， supervision signals were adopted at different network depths by adopting deep supervision strategy to improve training efficiency. Experimental results show that in coronary artery automatic segmentation task， the average Dice Similarity Coefficient （DSC） of the proposed network reaches 87.16%， which is 4.04 and 2.31 percentage points higher than those of nnU-Net and Swin UNETR （Swin UNEt TRansformers）， and the average 95% Hausdorff Distance （HD95） of the proposed network reaches 3.69 mm， which is 14.43 mm and 13.75 mm lower than those of nnU-Net and Swin UNETR. It can be seen that the proposed network can improve segmentation accuracy of coronary artery structure effectively， and help clinicians to understand the coronary artery structure of patients more accurately， so as to evaluate the disease more effectively.