For　power　system　transient　stability　assessment,　the　characteristic　differences　of　critical　samples　are　not　obvious　in　the　early　stage　after　fault　clearance,　and　the　prediction　accuracy　is　low.　Over　time,　the　evaluation　accuracy　improves,　while　the　timeliness　of　the　evaluation　is　difficult　to　ensure.　Aiming　at　the　contradiction　between　the　accuracy　and　timeliness　of　transient　stability　assessment,　a　time-adaptive　transient　stability　assessment　method　of　power　system　based　on　ensemble　learning　is　proposed.　First,　the　unbalanced　data　are　sampled　by　the　EasyEnsemble　algorithm.　Ensemble　LSTM　classifiers　with　different　evaluation　cycles　are　trained.　Thus,　the　stability　prediction　results　of　samples　in　different　evaluation　cycles　are　output.　Second,　the　evaluation　moments　are　divided　and　multi-stage　threshold　classification　rules　are　proposed.　The　threshold　is　adjusted　adaptively　to　evaluate　the　reliability　of　the　prediction　results.　Finally,　the　samples　whose　prediction　results　are　evaluated　as　unreliable　are　handed　over　to　the　model　of　the　next　evaluation　cycle　for　judgment　until　the　reliability　reaches　the　threshold.　The　simulation　results　in　the　IEEE39　bus　system　show　that　the　proposed　method　has　better　performance　than　other　time-adaptive　methods;　in　the　case　of　unbalanced　samples,　the　method　achieves　a　better　correction　effect.　©　2022　Power　System　Protection　and　Control　Press.　All　rights　reserved.