The ground conditioning effect is the key to shield metro tunneling speed. As a result, the testing results of the slump, penetration and resistivity are taken as the data sample set. And then the training set, verification set and prediction set are divided into 6∶2∶2 according to the data partitioning method commonly used in the field of deep learning. Finally, a prediction model for ground conditioning effect is established based on the BP neural network, and the model is applied to the ground conditioning effect prediction of waterrich sandy stratum in Nanchang. The study results show that: (1) The average predicted values of slump, permeability coefficient and internal friction angle during model learning are 1728 mm, 3355×10-6 cm/s, and 216°, respectively, and the average relative errors are 176%, 453%, and 360%, respectively. (2) The output of the prediction set coincides with part of the measured data; the average errors of the slump, permeability coefficient and internal friction angle are all within 5%; and the determinable coefficients R2 are 088, 090, and 085, respectively, which indicates that the neural network structure is a highprecision model. The errors of the prediction results are within the allowable error range of the ground conditioning, which shows that the BP neural network model can accurately predict the effect of ground conditioning.