An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented. This method deduces the recursive prediction error (RPE) learning algorithm of SRNN,and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control,due to its less weight values,simpler learning algorithm,higher identification speed,and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicated topological structure in usual dynamic recurrent neural network.