This paper discusses the sensitivity and uncertainty analysis methods of multi-parameters related to model parameters and model simulation performance. Multi-parameter sensitivity analysis based on Monte Carlo simulation can evaluate the relative importance of multiple parameters in the model. Sensitivity uncertainty analysis can quantitatively evaluate model performance. The case study shows that the number of optimized parameters can be reduced by the sensitivity analysis method. Moreover, before the parameter optimization of the model, the simulation accuracy of the model can be determined based on the sensitivity analysis. For example, the same model can obtain higher simulation accuracy in Chaohe River than in river. The difference of this same model in different watersheds is due to the imperfection of the model structure on the one hand, and the data errors used for modeling on the other hand. The parameter optimization results show that the parameter sensitivity and the overall performance evaluation method of the model are reasonable.