Swirling　vortex　rope　in　draft　tube　(DT)　is　a　typical　hydraulic　instability　of　a　pump　turbine　(PT)　in　the　pumped　storage　plant　(PSP).　In　view　of　the　potential　hazards　of　the　vortex　rope,　accurate　recognition　of　its　intensity　is　of　great　significance　to　maintain　the　stable　operation　of　the　PT.　Due　to　the　limitations　of　shallow　learning　algorithms　during　intelligent　recognition,　an　adaptive　deep　learning　framework　is　innovatively　proposed　in　this　study.　Firstly,　the　measured　high-precision　pressure　fluctuation　signals　based　on　the　prototype　PT　in　a　Chinese　PSP　that　can　reflect　different　intensities　of　vortex　ropes　in　the　DT　are　utilized　as　the　input　data.　Secondly,　a　preliminary　deep　learning　framework　that　integrates　convolutional　neural　network　(CNN),　bidirectional　long　short-term　memory　(BiLSTM)　and　multi-head　self-attention　mechanism　(MHSAM)　is　constructed.　Then,　the　Bayesian　optimization　algorithm　(BOA)　is　utilized　to　adaptively　determine　several　hyperparameters　of　the　framework.　And　an　adaptive　BOA-CNN-BiLSTM-MHSAM　framework　is　established　to　recognize　different　intensities　of　vortex　ropes　in　the　DT.　Finally,　the　recognition　performance　of　the　proposed　framework　is　demonstrated　through　comparing　with　other　deep　learning　frameworks.　And　the　recognition　results　illustrate　that　the　proposed　BOA-CNN-BiLSTM-MHSAM　framework　can　be　utilized　to　effectively　recognize　different　intensities　of　vortex　ropes　in　the　DT.　It　will　be　a　good　technical　reserve　to　improve　the　intelligent　level　of　the　monitoring　system　of　the　PSP.　©　2024　Elsevier　Ltd