Abstract: Aiming at the identification of complex nonlinear dynamic system,an identification model and method based on process neural network(PNN) is proposed.According to the model structure which is to be identified and the dynamic sample data which reflect system modal verification characteristics,a system identification model based on PNN is set up us-ing nonlinear transform mechanism and self-adaptive learning ability of PNN to the relationship between time-varying input signals and output signals.The identification model can reflect spatial weighted aggregation and time effect accumulation result to multi-input time-varying signals at the same time,and the dynamic input-output mapping relationship of nonlinear system can be found directly.A PNN model for parallel structure and serial-parallel structure is constructed,and the cor-responding learning algorithm and realization mechanism are given.The experiment results verify the effectiveness of the model and algorithm.