Based on trend analysis, multiple linear regression and BP neural network, a combined model was established to predict the industrial water demand of 2030 in the receiving area of the East Route of the South-to-North Water Diversion Project in Jiangsu Province by the relative error-inverse distance weight method with 2020 as the current year. The results show that the deviation between the predicted values of industrial water demand based on trend analysis, multiple linear regression and BP neural network is less than 10%, and the average error between each method and the real value is less than 10%. The coefficient of determination (R2) of industrial water demand obtained by the combined prediction model is 0.02-0.09 higher than that of any individual prediction model. The predicted value of the total industrial water demand of Jiangsu section in 2030 is 14.68×108 m3, an increase of 70.3% compared with the current year（2020）. The results of this study can not only provide reliable prediction data for the East Route of the South-to-North Water Diversion Project in Jiangsu Province, but also provide a reference method for industrial water demand prediction in other receiving areas of the project.