Artificial neural network (ANN), ordinary kriging (OK) and sequential Gaussian simulation (SGS) was introduced separately to simulation, interpolation and prediction of spatial distribution of soil salinity in a field typical of the coastal polder in North Jiangsu, to work out optimal structures and parameters of various methods for comparison of the methods in efficiency of predicting distribution characteristics and spatial structure of soil salinity. Results show that all the three methods, ANN, OK and SGS, were quite good at simulating and predicting spatial distribution of soil salinity and displayed quite high accuracy in the simulation, interpolation and prediction. The spatial distribution obtained by the ANN method was the most continuous and smooth, while that obtained by the SGS method was relatively discrete and fluctuant. The ANN method exhibited a relatively high prediction accuracy at sites of low soil salinity, but a much lower accuracy than the SGS and OK did at sites of high soil salinity. Furthermore, the prediction of SGS tallied the most with the fluctuation trait of measured value, and the narrowest fluctuation range was observed in prediction using the ANN method. The SGS method was better than the ANN and OK methods at reflecting spatial structure and fluctuation of the random variables of data, indicating that SGS is superior to ANN and OK as a whole. The findings may be cited as reference for precision assessment and high-efficiency amelioration of saline soil in coastal polders.