Abstract: A typical case with chlorobenzene contamination in a chemical factory relocation site in Shaoxing city Zhejiang Province, was selected, and some nested well groups (1~5, 5~9 and 10~14m) was set up to investigate the concentration of chlorobenzene in groundwater in three-dimensional space.On the basis of statistical analysis of the survey results, the 3D spatial interpolation calculation of chlorobenzene concentration in groundwater was performed by ordinary Kriging interpolation method (OK), inverse distance weight interpolation method (IDW), improved inverse distance weight interpolation method (IDWN) and nearest neighbor interpolation method (NN), respectively. The optimal interpolation method was selected to study the three-dimensional spatial distribution of chlorobenzene, after the comparison of the prediction accuracy of different interpolation models and parameters, and the comparison of the amount of groundwater above certain management target value and the concentration characteristics of chlorobenzene obtained by different calculation methods. The results showed that the spatial distribution of chlorobenzene content in groundwater varied greatly at different depths, and that the concentration of chlorobenzene in groundwater decreased gradually with the increase of depth. The maximum detection concentration was 8050 μg/L, and that the average concentrations were 484.45 μg/L, 230.43 μg/L and 23.08 μg/L, at three different depths. The results also showed that there existed a certain positive global spatial autocorrelation relationship in the concentration of chlorobenzene in groundwater, and the accuracy of IDWN prediction was the higher than other methods, and the interpolation accuracy was the highest when the vertical anisotropy ratio coefficient was 3 and the weight function was 6. The amount of contaminated groundwater above the third level of groundwater quality standard in china and the average concentration of chlorobenzene were suitable, estimated by this interpolation method, which can better reflected the spatial distribution characteristics of pollutants.