Feature　extraction　and　intelligent　recognition　of　the　vibration　signals　of　pump　turbines　are　significant　to　reliable　and　safe　operation　of　a　pumped　storage　power　station.　Due　to　its　complicated　operational　conditions,　a　pump　turbine　in　operation　can　create　a　large　number　of　physical　sources　that　excite　its　vibrations,　and　the　frequency　components　of　the　vibration　signals　are　quite　complicated.　The　traditional　methods　suffer　a　poor　accuracy　of　feature　extraction　from　a　complicated　vibration　signal.　To　improve　the　accuracy,　this　paper　describes　a　new　model　of　feature　extraction　and　intelligent　recognition　of　the　vibration　signals,　based　on　the　variational　mode　decomposition　(VMD),　bubble　entropy　(BE),　and　long　short-term　memory　(LSTM)　neural　network.　First,　this　method　analyzes　the　vibration　signal　using　VMD　and　obtains　several　modes.　Then　for　each　mode,　its　BE　value　is　calculated　and　a　BE　eigenvector　is　constructed.　Finally,　the　eigenvectors　of　the　vibration　signal　are　trained　and　recognized　using　a　LSTM　neural　network.　We　have　verified　the　method　against　the　complicated　vibration　signals　measured　at　the　top　cover　of　a　pump　turbine　at　the　Pushihe　pumped　storage　station,　and　achieved　a　signal　recognition　accuracy　of　97.87%,　indicating　its　important　engineering　application　value.　©　2023　Tsinghua　University　Press.　All　rights　reserved.