Excessive experimental costs lead to fewer experimental sample points obtained for complex systems with nonlinear multipolar outputs， and lower accuracy of proxy models. An experimental design and modeling method based on priori information was proposed to address this issue. Priori information was utilized to divide experimental design regions， weighted hesitant fuzzy set was constructed for each region based on volatility indicator to increase the rationality of evaluating results. The number of experimental sample points was determined by combining volatility and range of each region， and the sample points were obtained by Hammersley sequence sampling. Then staged search Particle Swarm Optimization （PSO） algorithm was combined with Kriging method to improve the computational accuracy of proxy model. Finally， the damage model of simulating planar truss structure was used to verify the effectiveness of the proposed method. Experimental results show that the model goodness-of-fit of model established by the proposed method is improved by 0.84% and 4.94% on average and root mean squared error is reduced by 31.02% and 57.18% on average compared with the models built by Hammersley sequence sampling and Latin hypercube design.