In this study, based on soil profile data in Tianranwenyanqu Basin, we evaluated the effect of 12 familiar pedo-transfer functions according to the fundamental soil properties to estimate the saturated soil hydraulic conductivity, and then explore the applicability of the multiple regression and BP Artificial Neural Network. The results showed that the prediction accuracy of pedo-transfer functions based BP-ANN is much better than from multiple regression, Wosten1999 based BP-ANN has the highest prediction accuracy for the surface and the bottom layers, Li2007 for the second layer, while Wosten1999 based BP-ANN is the best model without layering. Besides, we use GIS spatial interpolation to express visually the saturated soil hydraulic conductivity at different depths, which could provide basic parameters for modeling soil water movements in this region.