The instability disaster of reservoir bank slope will cause huge losses to the benefit of the project and the safety of surrounding life and property, and the displacement monitoring data can directly characterize the safety status of reservoir bank slope. In view of the traditional deformation and displacement analysis, only a single monitoring point is considered, and the similarity and relevance of displacement between different monitoring points still need to be excavated. Based on the clustering method in the field of spatio-temporal data mining, considering the attribute characteristics and spatial characteristics of measuring points, K-means algorithm is used to measure the similarity between measuring points and realize the division of measuring points; based on the division of measuring points, the projection clustering algorithm optimized by genetic algorithm is used to map the high-dimensional data to the low-dimensional space. By extracting the characteristics of measuring point data, the purpose of screening the measuring points needing attention and compressing the data order is achieved. Based on practical engineering data, it is shown that the spatio-temporal clustering mining method is convenient and effective, and gradually reduces the monitoring data of slope displacement. The method can be used for monitoring data mining of similar reservoirs.