To solve the problem that the internal correlation between multiple measuring points cannot be considered by traditional single measuring point monitoring models, which is difficult to reflect the regional characteristics of high arch dam deformation in space. A high arch dam deformation prediction model based on clustering partition and a multi-output least square support vector regression machine (MO-LSSVR) algorithm is proposed. Based on the composite similarity index between the measuring points, the clustering partition of spatial correlation measuring points is realized by hierarchical agglomerative clustering (HAC) algorithm. The MO-LSSVR algorithm integrating the correlation characteristics of measuring points is then used to model the points in the partition. The engineering example results show that the clustering partition results are consistent with the spatial distribution characteristics of dam deformation. The MO-LSSVR model based on the reasonable partition results has high accuracy and robustness, which provides a new method to accurately predict the dam deformation and monitoring the overall safety state of the dam from the multi-measuring points correlation dimension.