Based on observed meteorological data, such as daily mean wind speed, daily mean air temperature, daily mean air humidity, daily mean water vapor pressure, daily mean total radiation, daily mean land surface temperature, daily mean rainfall, and daily mean evaporation, and daily mean soil moisture at 10 cm, 20 cm and 30 cm in depth, statistical relationships were established between meteorological variables and soil moisture using the Support Vector Machine (SVM) technique, and on such a basis, models for simulation and prediction of soil moisture were built up. It was found that responses of soil moisture to meteorological variables somewhat lagged behind, and were affected by soil depth. The model for prediction of soil moisture taking into account the lag correlation was more accurate than the one that did not count the lag correlation. Besides, using the meteorological variables, the model was more accurate in simulating and predicting the soil moisture at 10 cm in depth than in doing the soil moisture at 20 cm or 30 cm in depth. By taking into account the close relationships between the soils at 10 cm and 20 cm and between the soils at 20 cm and 30 cm in soil moisture, it is advisable to use the support vector machine technique in simulating and predicting soil moisture at 20 cm or 30 cm on the basis of the soil moisture at 10 cm or 20 cm. The findings indicate that the model for simulation of soil moisture is very high in accuracy.