The measured data-based dam monitoring model could be used to analyze and predict the dam's operational behavior providing a scientific basis for risk assessment and decision-making of dam management. The traditional dam safety monitoring models established by multiple regression, stepwise regression, time series, gray theory, and other statistical methods have poor robustness and generalization. For this reason, an optimized support vector machine (SVM) whose novelty lies in simple execution procedure, self-adaptive hyperparameter selection from adaptive position particle swarm optimization (APPSO), user-friendly implementation and retention of influence factor combination, is presented for constructing the dam safety monitoring model.