Abstract: A BP neural network could solve a non-linear relationship problem of the surface subsidence. In order to more accurately predict the surface subsidence deformation, an Adaboost algorithm was introduced to improve the BP neural network. A Matlab R2014a was applied to establish a surface subsidence prediction model with BP neural network based on the Adaboost. Firstly, a training and test were conducted with the BP neural network. After several iterations, each BP neural network as a weak predictor would be weighted and combined and then would form a strong predictor. A surface subsidence prediction was conducted in Baohe section tunnel of Qingdao Metro No.3 Line. The prediction results showed that the predicted average absolute error predicted with the BP neural network of the Adaboost was 0.585 3 mm, the average relative error was 5.82 %. In a comparison with the prediction of BP neural network, the absolute error was reduced by 2.594 7 mm and the relative error was reduced by 27.46 %. Therefore the BP neural network with the Adaboost could be suitably applied to the prediction of the surface subsidence prediction and the prediction accuracy would be higher.