Due to the complex geological conditions and external triggering factors, the deformation of landslide disaster often has spatial differences, especially in the Three Gorges Reservoir area. It is of great significance to explore the governing factors and their thresholds in different parts of landslide. In this research, the deformation of Shuping landslide is analyzed. First, 9 hydrological factors are selected for data mining analysis by comprehensive research, including 5 reservoir water factors and 4 rainfall factors. Then, the numerical variables are transformed into discrete variables by two-step clustering method, and the Apriori algorithm is was utilized to deal with the classified variables to generate the correlation criterion meeting the minimum confidence, and the correlation criterions between triggering factors and landslide displacement are established. Finally, the threshold of governing factors are mined out by decision tree C5.0 models. The results indicate that governing factors controlling the deformation of different parts of landslide are distinct. Specifically, the rear landslide is jointly controlled by the reservoir water and rainfall. On the contrary, the reservoir level controls the deformation of other parts of Shuping landslide. Generally, the daily drop of water level is the most important factor causing the deformation of Shuping landslide. During the period of low water level (138.951 ~ 147.437 m), once the daily drop of water level exceeded 0.416 m/d, the landslide will show severe deformation. This research reveals that the study of association criterion and threshold is of great significance for landslide deformation analysis. Data mining technology can be better applied to the prediction of the reservoir landslide.