The　changepoints　detection,　phased　trend　identification　and　deformation　cause　analysis　of　dam　deformation　monitoring　sequences　help　to　deepen　the　understanding　of　the　evolution　of　dam　deformation　in　different　time　scales,　which　is　of　great　significance　for　dam　safety　operation　and　management.　We　designed　the　process　for　analyzing　dam　deformation　monitoring　data　from　variational　mode　decomposition,　changepoints　detection,　segmental　trend　analysis　to　correlation　analysis,　and　used　this　process　to　analyze　the　ideal　time　series　simulation　signal,　the　standard　test　Bumps　simulation　signal,　and　inverse　plummet　deformation　monitoring　data　of　the　Datengxia　Dam　from　May　2022　to　May　2024　respectively.　The　variational　mode　decomposition　(VMD)　method　is　used　to　decompose　the　dam　deformation　monitoring　sequence　to　the　normal　dam　deformation　component(U1),　environmental　impact　component　(U2),　and　other　noise　influence　subitems　U3　and　U4　are　obtained,　which　effectively　avoids　the　interference　of　the　other　noise　terms　on　the　changepoints　detection　and　trend　identification.　On　this　basis,　improved　Bayesian　block　(BBLOCKS)　algorithm　is　used　to　detect　changepoints　in　component　U1,　and　U1　is　divided　into　sub-segments　with　stable　trends.　Then,　Mann-Kendal　trend　analysis　(MK)　and　innovative　trend　analysis　(ITA)　methods　are　used　respectively　to　identify　the　trend　of　each　sub-segment,　which　helps　to　analyze　the　reasons　for　the　changes　in　each　sub-segment　individually.　Finally,　correlation　analysis　was　done　between　the　U2　subseries　and　the　environmental　impacting　factors　such　as　temperature,　water　level　and　osmotic　pressure,　and　the　correlation　coefficient　between　the　U2　subseries　and　temperature　=　0.96,　which　has　the　highest　correlation,　and　is　the　main　cause　of　the　U2　subseries,　indicating　that　the　temperature　change　has　a　sustained　effect　on　the　deformation　of　the　dam.　Examples　show　that　the　dam　data　analysis　process　recommended　of　variational　modal　decomposition,　changepoints　detection,　segmental　trend　analysis,　and　correlation　analysis　of　environmental　impact　factors　effectively　decomposes　the　data　into　various　sub　components　and　sub-segments,　which　plays　an　important　role　in　trend　analysis　and　cause　analysis　of　deformation.　©　2024　IEEE.