Taking MPDI(modified permanent drawdown index)as an indicator of surface soil conditions under water utilization for human activities, a soil moisture neural network model with traditional natural factors was constructed to simultaneously consider the influence of natural elements and water use for human activities. The model was used to simulate the surface soil moisture in 2018 in Hebei Province. The results show that the simulation results of soil moisture content considering MPDI index are in good agreement with the measured values, with a correlation coefficient of 0. 7 in training period and 0. 5 in verification period. The soil moisture distribution of the neural network in a single day was analyzed and the correlation coefficient is 0. 67 for the example date, which indicates a good performance in revealing the spatial heterogeneity of soil moisture. The simulated results are consistent with the SMAP(Soil Moisture Active and Passive)products in Hebei Province, with higher values in summer and east plain, but with lower values in spring and northwest mountains.