  This paper presents an innovative Comprehensive Growth Dynamic Model (CGDM).
CGDM is designed to simulate the temporal evolution of an event, incorporating
economic and social factors. CGDM is a regression of logistic regression, power
law regression, and Gaussian perturbation term. CGDM is comprised of logistic
regression, power law regression, and Gaussian perturbation term. CGDM can
effectively forecast the temporal evolution of an event, incorporating economic
and social factors. The illicit trade in wildlife has a deleterious impact on
the ecological environment. In this paper, we employ CGDM to forecast the
trajectory of illegal wildlife trade from 2024 to 2034 in China. The mean
square error is utilized as the loss function. The model illuminates the future
trajectory of illegal wildlife trade, with a minimum point occurring in 2027
and a maximum point occurring in 2029. The stability of contemporary society
can be inferred. CGDM's robust and generalizable nature is also evident.
