Abstract: The levitation system of maglev train has intrinsic nonlinear characteristics. Under the action of track irregularities and nonlinear loading, the identification and processing ability of nonlinear term have an important influence on the suspension stability of the train. Especially when the track stiffness is weak, it is easier to affect the suspension stability under the action of small deformation, resulting in the phenomenon of point dropping and rail smashing. Starting from the nonlinear dynamic modeling of maglev train levitation system, the system parameter identification of nonlinear term is emphatically analyzed. Based on Hopfield neural network, the error function and network identification scheme are constructed. Combined with the parameter identification error function and the standard energy function of Hopfield network, corresponding network weight matrix is obtained, and the corresponding identification results are further derived. Through numerical simulation analysis, it can be found that the error between the results of the proposed identification method and the output results of the nonlinear term is very small, this identification method can better fit the parametric nonlinear term contained in the state equation, thus the reliability of the identification model is verified.