Abstract: In order to accurately predict the settlement of the wind turbine foundation and avoid the deformation of the wind turbine structure due to the excessive uneven settlement of the wind turbine foundation, which affects the safety and life span of the wind turbine, a vertical displacement prediction model of the wind turbine foundation based on a three-dimensional convolutional neural network (three-dimensional convolutional neural network, 3DCNN) is constructed, originated from a certain onshore wind turbine foundation reinforcement project. During the construction process of the proposed 3DCNN model, the vertical displacement monitoring data from the measurement points at different locations are spatio-temporally reconstructed firstly. The monitoring data is then imported into a spatio-temporal matrix, where the 3D convolutional kernel learns the spatio-temporal features between the data. Comparing with several prevalent neural network models, the 3DCNN model is found to have higher prediction accuracy in settlement prediction, as well as superior generalization and stability.