The present invention puts forward a time-varying neural dynamics system identification method based on chebyshev polynomial expansion. The time-varying neural dynamics system identification method comprises the steps of: representing a time-varying neural system consisting of simulation input/output spike potential sequences with a Volterra series, and representing feedforward and feedback kernel functions with different Volterra kernels; expanding the time-varying Volterra kernel with a Laguerre primary function to obtain a time-varying generalized Laguerre-Volterra model; expanding time-varying parameters of the time-varying generalized Laguerre-Volterra model with a chebyshev polynomial, so as to convert the time-varying model into a time-invariant model; and selecting significant model items by using a forward orthogonal regression algorithm, estimating time-invariant parameters by using a generalized linear fitting algorithm, and then obtaining the time-varying parameters and the original time-varying kernel functions through reverse solution. Compared with an adaptive filtering technology in the prior art, the time-varying neural dynamics system identification method of the present invention has a better tracking capability for a strong non-stationary neural system signal, can achieve accurate tracking for the time-varying system kernel functions, can achieve modeling for the neural system, especially provides a new research method for system modeling of massive high-dimensional data, and has an important meaning for revealing a complex neural dynamics mechanism for completing information processing by a cerebrum.