Abstract: Accurate spatial information about soil organic matter (SOM) is critical for farmland use and soil environmental protection. In order to find the best interpolation method of SOM at the provincial scale, here we proposed there methods, back propagation neural network combined with ordinary kriging (BPNN_OK, based on geographic coordinates, environmental factors and neighbor information as auxiliary variables), radial basis function neural network with ordinary kriging (RBFNN_OK, based on geographic coordinates, environmental factors and neighbor information as auxiliary variables) and ordinary kriging (OK), to predict the distribution of SOM. Environmental factors were extracted by digital terrain and remote sensing image analysis technique. The four-direction search method was applied to get the neighbor information. To establish and validate this method, 16 109 soil samples were collected during the project of soil-test-based formulated fertilization in Jiangxi Province in 2012 and randomly divided into two groups, as modeling points (13 693) and validation points (2 416). The results show that three methods produced the similar SOM maps. The error analyses indicated：Based on auxiliary variables and neural network model has greatly improved than OK method. Compare to OK, the root mean square errors (RMSE), mean absolute errors (MAE) and mean relative errors (MRE) of BPNN_OK were reduced 2.76 g/kg，2.34 g/kg，9.83%, RBFNN_OK were reduced 2.70 g/kg，2.29 g/kg，9.61%. This result suggested that it is helpful for improving the prediction accuracy to employ artificial neural network model in spatial prediction of SOM, and this model provides guidance how to select the model to predict soil nutrient at provincial scale, but could be improved in the future. Key words：soil organic matter; auxiliary variables; neural network model; simulation of spatial distribution