As the underlying foundation of digital twin networks (DTNs), digital twin channels (DTCs) can accurately depict electromagnetic wave propagation in the air interface, thereby supporting DTN-based 6G wireless networks. However, existing methods for constructing DTCs face challenges: the environmental information input to neural networks has high dimensionality, and the correlation between the environment and channels is unclear, leading to a highly complex relationship construction process.