In order to analyze the discrete dynamic modeling of intelligent teaching based on big data mining, a deterministic learning theory is put forward based on the research on the continuous excitation characteristics of radial basis function (RBF) neural network. Firstly, the hierarchical analysis framework of dynamic generative data in intelligent teaching is introduced. Then, the modeling /identification based on temporal data is discussed, and the similarity definition and fast identification method of temporal data sequence are studied. Finally, numerical experiments are carried out.The key to realize the local accurate modeling of discrete system dynamics lies in the satisfaction of some continuous excitation conditions, the exponential convergence of discrete linear time-varying systems, and the convergence of some neural network weights along the regression trajectory. These elements reveal the nature of deterministic learning for discrete dynamic systems. The local accurate modeling of discrete system dynamics can be used to time invariant representation of temporal data sequences. Numerical experiments on the fast recognition of temporal show that the error generated by the test mode is smaller than that of the other two when compared with the third training mode. The test temporal is most similar to the third training temporal.
