Aiming at the problems of strong nonlinearity, complexity of diagnosing and eliminating abnormal values, and poor ability to resist gross errors of traditional monitoring models for the analysis of dam deformation monitoring data, a dam deformation monitoring model based on robust estimate extreme learning machine(M-ELM)was established. It combines the theory of robust estimation which has strong roughness tolerance with the extreme learning machine which has strong ability in dealing with nonlinear problems. The network number of the hidden layers was determined by tests and a fourth power loss function was built. A weighted least square method was used to calculate the output weights, and the fitting and prediction of the original monitoring data was carried out. The monitoring data of a certain dam deformation was taken as an example for modeling analysis. The mean square error and the mean absolute percentage error reflecting the prediction accuracy, and the median absolute deviation representing the model robustness were taken as the evaluation indexes. The results show that each index of the dam deformation monitoring model based on robust estimate extreme learning machine is superior to other models.