The choice of the parameter rate setting objective function is crucial to the development of the basin hydrological model. In order to investigate the influence of different objective functions on the sensitivity of hydrological model parameters and uncertainty of runoff simulation, this paper takes the daily runoff simulation of Jinxi Chitan watershed in Fujian Province as an example, and uses the SCEM-UA optimization algorithm to rate the three-source Xin'anjiang model, and selects Nash efficiency coefficient (NSE), root mean square error (RMSE) and Kling-Gupta efficiency coefficient (KGE) were selected as the rate objective functions, and a combined multi-objective function F1, F2 and F3 with preferences for NSE, RMSE and KGE are also constructed for a total of six rate objectives to compare the differences in runoff simulation accuracy under different rate objectives.. And the Sobol global sensitivity analysis method was used to quantitatively compare and analyze the sensitivity of the Xin'anjiang model parameters under different rate objective functions. Finally, we analyzed the differences in runoff simulation uncertainties under different flow levels and abundant and dry periods based on the GLUE method. The results show that (1) the identification of the main sensitive parameters of the Xin'anjiang model under six rate-determined objective functions is consistent, and the multi-objective functions F1, F2, and F3 show consistency in parameter sensitivity and sensitivity ranking; (2) F1, F2, and F3 show higher runoff simulation accuracy than the single objective function, and the deterministic coefficients of F3 rate period and validation period reach 0.85, and the relative errors of total runoff are 0.45% and 7.12%, with the best simulation accuracy, F1 and F2. are the next best; (3) The uncertainty results of runoff simulation obtained with NSE as the rate objective show higher coverage and smaller uncertainty interval than other objective functions under different hydrological periods and different flow levels. The uncertainty of the parameters under different objective functions is the smallest in the dry period and the largest in the rich period, and the uncertainty of the runoff simulation increases with the increase of the flow level.