The　construction　of　an　interval　prediction　model　capable　of　explaining　deformation　uncertainties　is　crucial　for　the　long-term　safe　operation　of　dams.　High　effective　coverage　and　narrow　interval　coverage　widths　are　two　key　benchmarks　to　ensure　that　the　prediction　interval　(PI)　can　accurately　quantify　deformation　uncertainties.　The　vast　majority　of　existing　models　neglect　to　control　the　interval　coverage　width,　and　overly　wide　PIs　can　cause　decision　confusion　when　operators　are　developing　safety　plans　for　hydraulic　structures.　To　address　this　problem,　this　paper　proposes　a　novel　interval　prediction　model　combining　bidirectional　long-short-term　memory　network　(Bi-LSTM)　and　split　conformal　quantile　prediction　(SCQP)　for　dam　deformation　prediction.　The　model　uses　Bi-LSTM　as　a　benchmark　regressor　to　extract　and　explain　the　nonlinear　feature　of　dam　deformation　in　the　continuous　time　domain.　SCQP　is　used　to　quantify　the　uncertainties　in　dam　deformation　prediction　to　ensure　that　the　constructed　PI　can　achieve　high　effective　coverage　while　further　improving　the　accuracy　of　the　quantification　of　deformation　uncertainties.　The　effectiveness　of　the　proposed　model　is　validated　using　deformation　monitoring　data　collected　from　an　arch　dam　in　China.　The　results　show　that　the　average　prediction　interval　effective　coverage　(PICP)　of　the　proposed　model　is　as　high　as　0.951　while　the　mean　prediction　interval　width　(MPIW)　and　coverage　width-based　criterion　(CWC)　are　both　only　5.815　mm.　Compared　with　other　models,　the　proposed　method　can　construct　higher-quality　PIs,　thus　providing　a　better　service　for　the　safety　assessment　of　dams.