Abstract: Path loss refers to the attenuation of signal power during wireless signal propagation caused by factors such as distance and obstacles, and it holds significant importance for wireless communication systems. However, in complex electromagnetic environments such as substations, densely deployed power equipment leads to significant multipath effects, making traditional channel modeling methods inadequate for accurate signal propagation characterization. To enhance the prediction accuracy of path loss in complex environments such as substations, an environment-analytical fusion prediction method is proposed. First, based on the characteristics of scatterers in practical scenarios, we establish descriptive models for both regular and irregular scatterers. Next, a three-dimensional simulation environment is created within the station using the distribution of regular scatterers. Ray tracing (RT) technology is employed to perform preliminary calculations of path loss. The error of path loss by RT is calculated based on measured path loss data. Then, integrating environmental features such as corner angles and occlusion depths of irregular scatterers within the environment, a path loss prediction model based on radial basis function (RBF) neural networks is established. Finally, the performance of proposed model is validated using measurement data at 2.4 GHz in the substation. Simulation results demonstrate that the root mean square error (RMSE) is reduced by approximately 5.28 dB, which effectively improves the prediction accuracy of wireless channel path loss in complex environments.