The segmentation of cardiac anatomy represents a crucial stage in accurate diagnosis and subsequent treatment planning for patients with congenital heart disease (CHD). However, the current deep learning-based segmentation networks are ineffective when applied to 3D medical images of CHD because of the limited availability of training datasets and the inherent complexity exhibited by the variability of cardiac and large vessel tissues. To address this challenge, we propose a novel hybrid layer-based encoder-decoder framework for 3D CHD image segmentation. The model incorporates a global volume mixing module and a local volume-based multihead attention module, which uses a self-attention mechanism to explicitly capture the local and global dependencies of the 3D image segmentation process. This enables the model to more effectively learn the shape boundary features of organs, thereby facilitating accurate segmentation of the whole heart (WH) and great vessels. We compare our method with several popular networks on the public ImageCHD and HVSMR-2.0 datasets. The experimental results show that the proposed model achieves excellent performance in WH and great vessel segmentation tasks with high Dice coefficients and IoU indices.