Dynamic　displacement　is　an　important　physical　quantity　in　the　fields　of　seismic　engineering，military　weapon　design，and　structural　health　monitoring.　In　the　actual　test　process，the　acceleration　can　usually　be　directly　measured.　Due　to　the　uncertain　test　conditions　such　as　the　environment，the　acceleration　signal　is　unavoidable　contains　low-frequency　and　high-frequency　noise，which　causes　a　significant　drift　in　velocity　and　displacement　during　the　acceleration　integration　process.　Based　on　the　theoretical　frame⁃　work　of　Bayesian　inference，a　Bayesian　learning　dynamic　displacement　identification　method　is　constructed.　The　results　show　that，the　displacement　response　obtained　by　inversion　for　different　noise　conditions（white　noise，artificial　noise）is　basically　consistent　with　the　analytical　displacement；the　displacements　of　inversion　of　acceleration　sensor　signals　with　different　performances　are　com⁃　pared　by　using　a　large　shaking　table　test　data，and　their　uncertainty　is　analyzed.　The　results　show　that　this　method　has　certain　ad⁃　vantages　in　the　characterization　of　the　acceleration-displacement　relationship，and　can　achieve　the　displacement　solution　without　re⁃　lying　on　the　processing　of　the　acceleration　signal，thereby　avoiding　the　displacement　integral　distortion　caused　by　the　accumulated　noise　error.　©　2023　Nanjing　University　of　Aeronautics　an　Astronautics.　All　rights　reserved.