An　accurate　dam　deformation　prediction　model　is　vital　to　a　dam　safety　monitoring　system,　as　it　helps　assess　and　manage　dam　risks.　Most　traditional　dam　deformation　prediction　algorithms　ignore　the　interpretation　and　evaluation　of　variables　and　lack　qualitative　measures.　This　paper　proposes　a　data　processing　framework　that　uses　a　long　short-term　memory　(LSTM)　model　coupled　with　an　attention　mechanism　to　predict　the　deformation　response　of　a　dam　structure.　First,　the　random　forest　(RF)　model　is　introduced　to　assess　the　relative　importance　of　impact　factors　and　screen　input　variables.　Secondly,　the　density-based　spatial　clustering　of　applications　with　noise　(DBSCAN)　method　is　used　to　identify　and　filter　the　equipment　based　abnormal　values　to　reduce　the　random　error　in　the　measurements.　Finally,　the　coupled　model　is　used　to　focus　on　important　factors　in　the　time　dimension　in　order　to　obtain　more　accurate　nonlinear　prediction　results.　The　results　of　the　case　study　show　that,　of　all　tested　methods,　the　proposed　coupled　method　performed　best.　In　addition,　it　was　found　that　temperature　and　water　level　both　have　significant　impacts　on　dam　deformation　and　can　serve　as　reliable　metrics　for　dam　management.