In order to conduct a medium-long term simulation and prediction of the soil salt dynamics in the Yangtze River estuary, a nonlinear artificial neural network response-model among 6 factors of soil salt, rainfall, evaporation, river water EC, freshwater EC, water table and groundwater EC was established with the BP network which has been applied maturely and widely in all kinds of artificial neural networks. The structure of the network model was fixed on 6-11-1. The cells number of the hidden layer was confirmed using the Trial-and-Error method. After the network model was trained and tested by choosing appropriate parameters, it was applied to predict the average root-layer soil EC in the Yangtze River estuary in every month of 2003. The predicted result from the network model was compared with that from the linear regression model. Results showed that the BP network model had higher precision in predicting the salt dynamics than the linear regression model. The average relative error was 7.3% and the correlativity between predicted value and actual value was all right, both of which showed that the simulation and prediction of the BP artificial neural network could meet the need of the practical application.