Abstract：We adopt the gravity observation data of the southwestern margin of Ordos to train the long short-term memory recurrent neural network (LSTM). The results show that LSTM can obtain good estimation results based on limited data. Through comparing and analyzing the estimated results of LSTM and ordinary Kriging method based on free-air gravity anomaly data, we find that the estimation ability of neural network is better than ordinary Kriging method, but Kriging method performs better in terms of computing efficiency. Using the free-air gravity anomaly data to estimate the entire area, we show that LSTM method is significantly better than Kriging method. Adding elevation data as a constraint can effectively improve the accuracy of free-air gravity anomaly field estimated by LSTM method.