Dam deformation is usually affected by many factors, and monitoring data show certain non-stationarity and randomness. To improve the accuracy of dam deformation prediction, a combined prediction model of dam deformation based on optimized variational mode decomposition was proposed. The model first used PSO (particle swarm optimization) to find the optimal hyperparameters of VMD (variational mode decomposition) . Then the dam deformation was decomposed into trend item, periodic item, and random item components. According to the timing characteristics of each component after decomposition, a combination of TCN (temporal convolutional network) and LSTM (long short-term memory network) was used for prediction. The final predicted value was obtained by reconstructing and adding the predicted values of each component. Taking actual engineering data as an example, the model was quantitatively evaluated using indicators such as MAE (mean absolute error) , MSE (mean square error) , and MAPE (mean absolute percentage error) . It was also compared with a single forecasting model. The results show that the dam deformation combination prediction model based on optimized variational mode decomposition proposed in this paper has higher accuracy. It can effectively extract the hidden information features in the dam deformation data, and reduce the non-stationarity of the dam deformation time series data. It has a high value of popularization and application and provides reference and guidance for accurately predicting dam deformation.