Deformation data directly characterizes the safety condition of concrete arch dam. However, traditional deformation analysis targets only a single monitoring point. The similarity and association of deformation between different monitoring points still need to be excavated. In this research, a clustering analysis model for the deformation of high concrete arch dam is established based on panel data analysis method. The deformation sequences of concrete arch dam are analyzed at first, and the similarities of temporal and spatial deformation sequences are extracted based on the clustering method in space-time data mining. Three similarity indicators, namely, absolute distance, incremental distance, and growth distance of the deformation sequences at different time sections and different positions as well as their corresponding comprehensive distance indicators are proposed to quantify the similarity between temporal and spatial sequences. Moreover, Ward's junction clustering method is adopted to divide the time periods and the corresponding deformation areas. Practical engineering case study verifies the rationality of the selected similarity indicators and the effectiveness of the model.