The　deformation　behavior　of　a　dam　can　comprehensively　reflect　its　structural　state.　By　comparing　the　actual　response　with　model　predictions,　dam　deformation　prediction　models　can　detect　anomalies　for　effective　advance　warning.　Most　existing　dam　deformation　prediction　models　are　implemented　within　a　single-step　prediction　framework;　the　single-time-step　output　of　these　models　cannot　represent　the　variation　trend　in　the　dam　deformation,　which　may　contain　important　information　on　dam　evolution　during　the　prediction　period.　Compared　with　the　single　value　prediction,　predicting　the　tendency　of　dam　deformation　in　the　short　term　can　better　interpret　the　dam’s　structural　health　status.　Aiming　to　capture　the　short-term　variation　trends　of　dam　deformation,　a　multi-step　displacement　prediction　model　of　concrete　dams　is　proposed　by　combining　the　complete　ensemble　empirical　mode　decomposition　with　adaptive　noise　(CEEMDAN)　algorithm,　the　k-harmonic　means　(KHM)　algorithm,　and　the　error　minimized　extreme　learning　machine　(EM-ELM)　algorithm.　The　model　can　be　divided　into　three　stages:　(1)　The　CEEMDAN　algorithm　is　adopted　to　decompose　dam　displacement　series　into　different　signals　according　to　their　timing　characteristics.　Moreover,　the　sample　entropy　(SE)　method　is　used　to　remove　the　noise　contained　in　the　decomposed　signals.　(2)　The　KHM　clustering　algorithm　is　employed　to　cluster　the　denoised　data　with　similar　characteristics.　Furthermore,　the　sparrow　search　algorithm　(SSA)　is　utilized　to　optimize　the　KHM　algorithm　to　avoid　the　local　optimal　problem.　(3)　A　multi-step　prediction　model　to　capture　the　short-term　variation　of　dam　displacement　is　established　based　on　the　clustered　data.　Engineering　examples　show　that　the　model　has　good　prediction　performance　and　strong　robustness,　demonstrating　the　feasibility　of　applying　the　proposed　model　to　the　multi-step　forecasting　of　dam　displacement.　©　2023　by　the　authors.