Landslide prediction is important for mitigating geohazards but is very challenging. The fluctuation of reservoir water level and rainfall are the main external triggering factors controlling the deformation of riverine landslides. In this paper, the Baishuihe landslide in the Three Gorges Reservoir area, which has a typical “step-like” behavior, is taken as the study case, and an integrated approach for landslide displacement prediction combining data mining and Variational Mode Decomposition, Fruit Fly Optimization Algorithm, Back Propagation Neural Network (VMD-FOA-BPNN) is proposed. Nine triggering factors including the reservoir level and rainfall are extracted. First, triggering factors and monthly velocity of the landslide are clustered into several categories by Two-step Clustering (TSC). Then, Apriori algorithm is used to mine the association rules between triggering factors and monthly velocity, and comprehensive contribution of each factor is calculated based on the data mining results. Next, the displacement of monitoring point ZG93 and triggering factors are decomposed by VMD based on the time series analysis of the landslide. Last, the trend term displacement is trained and predicted by the subsection functions, and FOA-BPNN models are used to train and predict the periodic and random term. The prediction results show that, compared with the current popular prediction models, the proposed model can effectively improve the prediction accuracy, which has high practicability and application value in the study of landslide displacement prediction.