During　its　long　service　life,　an　arch　dam　affected　by　a　combination　of　factors　exhibits　a　typical　time-varying　characteristic　in　terms　of　its　structure　and　material　properties,　and　the　deformation　in　the　dam　structure　can　directly　and　reliably　reflect　the　health　and　service　status　of　dams.　Therefore,　an　accurate　deformation　prediction　is　an　important　part　of　dam　safety　monitoring.　However,　due　to　multiple　factors,　dam　deformation　data　often　tend　to　be　highly　volatile,　and　most　existing　deformation　estimation　techniques　employ　a　single　algorithm,　which　may　not　effectively　capture　the　potential　change　process.　A　hybrid　model　for　dam　deformation　prediction　has　been　proposed　to　overcome　this　problem.　First,　dam　deformation　data　are　decomposed　into　three　components　by　seasonal　and　trend　decomposition　using　loess.　Second,　a　convolutional　neural　network-gated　recurrent　unit　(GRU)　hybrid　model,　which　optimizes　hyperparameters　using　the　sparrow　search　algorithm,　is　used　to　capture　the　nonlinear　relationships　that　exist　in　each　component.　Finally,　the　final　prediction　result　of　dam　deformation　is　the　comprehensive　output　of　multiple　submodules.　The　deformation　monitoring　data　(period:　2009-2019)　of　a　parabolic　variable-thickness　double-curved　arch　dam　located　in　China　are　considered　as　the　survey　target.　The　test　results　indicate　that　the　proposed　model　is　suitable　for　short-term　and　long-term　prediction　and　outperforms　other　models　in　terms　of　higher　robustness　to　abnormal　sequences　than　other　conventional　models　(R-2　differs　by　5.50%　and　7.87%,　respectively,　in　short-term　and　long-term　predictions　for　different　measurement　points,　while　other　models　differ　by　9.78%　to　reach　15.71%,　respectively).　Among　the　models　studied,　the　GRU　shows　better　robustness　to　abnormal　series　than　the　LSTM　with　good　prediction　accuracy,　fewer　parameters,　and　a　simpler　structure.　Hence,　the　GRU　can　be　employed　for　dam　deformation　prediction　in　practical　engineering.