Quantitative prediction of soil heavy metal content and evaluation of its uncertainties is of great practical significance to ecological risk assessment. In this study, a total of 1 155 soil samples for Hg content analysis were sorted into two disjoint datasets, a simulation dataset of 309 samples and a validation dataset of 846 samples. In order to validate accuracy and reliability of the results of the usage of the Sequential Gaussian Conditional Simulation (SGCS) method, comparison was performed of this method with the Simple Kriging Interpolation method based on the same Semi-variance model parameters. Furthermore, Sequential Gaussian Indicator Simulation (SGIS) was used to delineate areas with soil Hg content beyond the threshold value, and to explore for uncertainties with single points and multi-point combination. Results show that the E-type of 100 rounds of SGCS and the SK prediction are quite similar, the difference between the two is small in Average Prediction Error and Root Mean Square Error. This paper sampled the 1 ^st, 25 ^th, 50^th, 75^th, and 100^th round of single implementation of the SGCS method for comparison with the 846 points in the verification dataset in simulated value and interpolated value. The Mean Prediction Error and the Mean Square Prediction Error of the five rounds of single implementation are coincidentally and relatively higher, (within the range of 0~ 0.01). When threshold was set to be 0.15 mg kg^-1, the single-point critical probability is relatively higher. However, its confidence level is by a certain degree not enough to delineate polluted areas. Multi-point joint probability should be used to evaluate the reliability of the contaminated area.