In　order　to　adapt　to　the　development　of　automated　dam　monitoring　systems,　artificial　intelligence　(AI)　models　based　on　deep　learning　are　used　for　dam　deformation　prediction,　effectively　overcoming　the　limitations　of　traditional　statistical　analysis　methods.　These　AI　models　have　demonstrated　excellent　performance　in　capturing　the　deformation　characteristics　and　potential　dependencies　of　dams　during　their　long-term　service.　This　study　takes　a　certain　rolled　concrete　gravity　dam　as　an　object　and　combines　the　dam　deformation　statistical　model　with　the　Informer　deep　learning　model　to　propose　an　attention　mechanism　dam　deformation　prediction　model　that　considers　water　pressure,　temperature,　and　aging　factors.　The　comparative　analysis　of　model　performance　indicates　that,　compared　with　commonly　used　models,　such　as　multiple　linear　regression,　support　vector　machines,　and　long　short-term　memory　neural　networks,　the　proposed　model　can　better　adapt　to　the　complex　nonlinear　relationship　between　dam　deformation　and　influencing　factors　and　has　excellent　predictive　performance　and　generalization　ability,　effectively　improving　the　accuracy　of　dam　deformation　prediction　and　providing　practical　engineering　benefits　for　dam　safety　monitoring.　©　Published　under　licence　by　IOP　Publishing　Ltd.