The number of safety monitoring points of large-scale concrete dams is large and widely arranged. The analysis of the massive monitoring data makes it difficult to assess the safety state of dams timely and accurately, so the cloud model theory is introduced to analyze the difference and correlation of deformation monitoring data characterizing concrete dams. The monitoring data of each monitoring point of the dam is regarded as a cloud, and the monitoring values are regarded as cloud drops. The cloud parameters are then calculated to obtain the cloud numerical characteristics of the monitoring value sequence. Case analysis shows that the abnormal monitoring points in similar dam sections can be quickly discovered by the cloud similarity, and the clustering analysis of cloud similarity coefficients can be used to group the monitoring points. The grouping management of monitoring points can effectively improve the analysis efficiency of the monitoring data while realizing the correlation analysis of dam deformation properties.