Abstract: Dirichlet process mixture of Gaussian process model was proposed to reveal the intrinsic mechanism of multimodel of complex dynamic system architecture data. As for the difference between the mean structure and covariance structure of sparsity, parametric a priori and nonparametric a priori were designed based on the hybrid sampling framework of Polya urn sampling and overrelaxed sliced sampling. The hybrid sampling will not only be implemented under the unified MetropolisHasting probability evaluation criteria , but also be able to overcome the shortcomings of Gaussian random walk. Markov chain samples can be quickly and easily extended. The simulation results show that the hybrid sampling algorithm has more extensive adaptabilities and more accurately predictive effect than that of Gaussian process regression model and GPFR mixture model.