Abstract: It’s an important management measure for raising yield of proterandrous walnut that excessive staminate catkin of walnut (SCW) was thinned. The model of quadratic polynomial regression equation and BP artificial neural network was developed to apply SCW thinning measure. The effect of three independent parameters, namely ethrel, gibberellin and mepiquat on the average thinned percentage of SCW were considered with field experiment in which central composite design (CCD) and quadratic polynomial regression equation were employed. BP artificial neural network was developed to predict result of field experiment on the base of field model experiment and BP model of validation testing was carried out in the next year. Total 20 sets data of field model experiment CCD quadratic rotation regression design method were divided randomly into training group (17 sets data) and validating group (3 sets data) respectively, the data of validation testing was employed as testing group and the topological structure of BP was 3-5-1. The experiment result indicated that: (1) The error of predicted value of obtained BP artificial neural network for validating group was 1.3550%, 0.4291%, 0.3538%, respectively. (2) Difference between BP predicted value and the value of field experiment validation test was 2.04% , while the difference between regression predicted value and the value of field experiment validation test was 3.12%. (3) Predictive accuracy of BP was 1.0% more than predictive accuracy of regression predicted. Based on the practical strategy of integrated quadratic polynomial stepwise regression equation with BP artificial neural network could distinguish the effect of independent parameters and improve the predictive accuracy.