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In order to study the spatial parameters of the distributive hydrological models among the ecological influences of regional farmland under the condition of water-saving practices in large scale irrigation district, the Bayesian neural networks and back-propagation artificial neural network models were applied to establish regional pedotransfer function models. Based on the relationship of measured soil characteristic contents, soil particle percentage, organic matter and bulk density, the adaptability of these two kinds of ANN models were evaluated through simulated and predicted values statistically, accompanied with the SWRC figures. Results indicated that the BP and BNN were both feasible PTFs methods. The training simulated accuracy of traditional BP model was better than that of BNN. However, the predicted accuracy of BNN model generally was better than the BP model. Furthermore, the predictive accuracy of BP model was significantly influenced by the number of input factor groups. But there were little influences on different input factors of BNN model. So, the BNN showed good robustness for the simple inputs. Besides, the predicted SWRC was better fitted with measured and VG fitted curve than that of ANN. Thus, the BNN model was better than the traditional artificial neural network model. It had better adaptability in the pedotransfer function establishment when only soil particle distribution was used. All showing that the BNN method was a practical method for regional pedotransfer function establishment.