This article proposes a backpropagation neural network(BPNN) parameter fitting method to simulate the mapping relationship between the parasitic capacitance parameters and the gate source voltage of GaN HEMT due to its nonlinear characteristics under the influence of drain source voltage. The input layer of the neural network model is based on the GaN HEMT large signal parameter model and data manual, which extracts the parameter sampling points of parasitic capacitance related to the variation of leakage source voltage. This neural network model adjusts the weights of neurons by supplying error values to the output end to create a neural network training set that can adapt to the system mapping relationship, predict the connection between gate source voltage and parasitic capacitance in the actual open state of GaN HEMT, and link the acquired experimental data to the input layer for transmission. The BPNN model yielded a relative prediction error of 9%, demonstrating the efficacy of this method in predicting the behaviors of GaN HEMT devices. This technique simplifies the complexity of parameter extraction from physical models, thus facilitating the analysis and prediction of misleading situations.