  As the underlying foundation of a digital twin network (DTN), a digital twin
channel (DTC) can accurately depict the process of radio propagation in the air
interface to support the DTN-based 6G wireless network. Since radio propagation
is affected by the environment, constructing the relationship between the
environment and radio wave propagation is the key to improving the accuracy of
DTC, and the construction method based on artificial intelligence (AI) is the
most concentrated. However, in the existing methods, the environment
information input into the neural network (NN) has many dimensions, and the
correlation between the environment and the channel relationship is unclear,
resulting in a highly complex relationship construction process. To solve this
issue, in this paper, we propose a construction method of radio environment
knowledge (REK) inspired by the electromagnetic wave property to quantify the
contribution of radio propagation. Specifically, a range selection scheme for
effective environment information based on random geometry is proposed to
reduce the redundancy of environment information. We quantify the contribution
of radio propagation reflection, diffraction and scatterer blockage using
environment information and propose a flow chart of REK construction to replace
the feature extraction process partially based on NN. To validate REK's
effectiveness, we conduct a path loss prediction task based on a lightweight
convolutional neural network (CNN) employing a simple two-layer convolutional
structure. The results show that the accuracy of the range selection method
reaches 90\%; the constructed REK maintains the prediction error of 0.3 and
only needs 0.04 seconds of testing time, effectively reducing the network
complexity.
