Abstract: In order to solve the problems of stormwater management model（SWMM）in calibration process,such as complex parameters and tedious process. This paper takes a certain block of Xining city as an example to establish SWMM model. Morris screening method is used to analyze the sensitivity of parameters,and artificial calibration is carried out according to the results of sensitivity analysis. In addition,BP neural network is used to calibrate the model,and parameter sensitivity is combined to optimize it. Through the analysis of three calibration schemes,the results show that the relative sensitivity of hydro-hydraulic module parameters is basically same,among which the more sensitive parameters are sub-catchment Area,Imperv and Destore-imperv. Moreover,the sensitivity of model parameters is different under different rainfall conditions. The optimized BP neural network parameter calibration method has the best simulation effect and the Nash coefficient is the largest. On the one hand,the method which combines with sensitivity to optimize BP neural network can improve the accuracy of BP neural network calibration,on the other hand,it can improve the efficiency of traditional artificial calibration.