Real-time 6 Degree-of-Freedom (DoF) pose estimation is of paramount importance for various on-orbit tasks. Benefiting from the development of deep learning, Convolutional Neural Networks (CNNs) in feature extraction has yielded impressive achievements for spacecraft pose estimation. To improve the robustness and interpretability of CNNs, this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure (PE-VAE) and a Feature- Aided pose estimation approach based on Variational Auto-Encoder structure (FA-VAE), which aim to accurately estimate the 6 DoF pose of a target spacecraft. Both methods treat the pose vector as latent variables, employing an encoder-decoder network with a Variational Auto-Encoder (VAE) structure. To enhance the precision of pose estimation, PE-VAE uses the VAE structure to introduce reconstruction mechanism with the whole image. Furthermore, FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired shape. Comparative evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed, showcasing the significant contribution of VAE structures to accuracy enhancement, and the additional benefit of incorporating global shape prior features.