Soil pH is of great importance for nutrient form and validity. It varies greatly from space to space for the impact of environmental factors on. Hence, precisely knowing the distribution of soil pH is significant to effectively utilizing soil, fertilizing reasonably, and precision agriculture. By comparison with ordinary least square(OLS), geographically weighted regression(GWR) was proposed by the paper for analyzing the distribution of soil pH at regional scale. The purpose was to know the prediction accuracy of GWR approach and its feasibility in predicting other soil properties. Soil pH was firstly tested using the collected soil samples at field, then the environmental factors, which were easily acquired and had close relation to soil pH, were taken into account for establishing regression function with soil pH. These factors were elevation, slope, NDVI, iron oxide index, the nearest distance from samples to river, and soil erosion intensity. After tested by multi-collinearity and stepwise regression among factors, the sieved factors were employed for GWR analysis. The performances of GWR and OLS were estimated and discussed by using mean error, root mean square error, correlation coefficient (R2) between the predicted soil pH and observed pH at validation sites. The results show that the estimation accuracy by GWR has been greatly improved than that of by OLS. It can significantly lower AIC, largely improve coefficient of determination(Adj–R2) and decrease residuals sum of squares(RSS). The map interpolated by GWR was satisfactory in the appearance of continuous surface and gradual transition. It is concluded that GWR, by incorporating environmental factors and sample sites, is a promising method in predicting and mapping the spatial distribution of soil properties with great variations in space.