In　modern　industry,　the　health　monitoring　of　unit　equipment　is　crucial.　Due　to　the　complexity　of　unit　equipment,　there　exist　intricate　relationships　among　various　parameters　sensed　by　sensors,　which　influence　the　operational　state　of　the　unit.　To　enhance　the　accuracy　of　short-term　unit　state　prediction,　this　paper　proposes　a　joint　framework　based　on　CNN,　LSTM,　and　attention　mechanism.　In　this　study,　we　introduce　a　parallel　expanded　framework　where　CNN　captures　complex　dependencies　among　variables　instead　of　conventional　matrix　transformations　to　obtain　the　required　Q　in　the　attention　mechanism,　LSTM　captures　the　temporal　correlations　among　data　instead　of　K,　while　the　sequence　itself　serves　as　the　value　vector　V.　The　attention　mechanism　balances　the　importance　of　features　for　prediction.　Through　multiple　comparative　experiments,　the　proposed　model　demonstrates　higher　prediction　accuracy　compared　to　other　methods,　showing　promising　reliability　in　predicting　unit　states　based　on　sensor　parameters.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.