Segmentation of coronary arteries is crucial for the rapid diagnosis of cardiovascular diseases. Given the challenges posed by the complex structure of coronary arteries and the interference from other vascular tissues, which often result in fragmented segmentation, ensuring the model′s ability to adapt to segmenting different morphological structures of the coronary artery, a novel 3D coronary artery segmentation network (CA-SegNet) is proposed. This model incorporates a combination of CNN and Transformer as the encoder and decoder, leveraging their advantages and complementarity to fully extract both global and local features of coronary arteries. By proposing a multi-scale feature interaction module, the model simultaneously extracts multi-scale features of coronary arteries while facilitating feature channel interaction. In the decoding stage, an attention weighted feature fusion module is proposed to weight and fuse features from both spatial and channel perspectives, enabling the model to focus more on the coronary artery regions. Experimental results demonstrate that the proposed model achieves DSC, Recall, Precision, and HD95 values of 81.96%, 84.24%, 80.11% and 14.94 respectively, surpassing current popular segmentation models and validating the effectiveness of CA-SegNet.