The　performance　variations　of　aero-engine　is　a　key　concern　for　airlines,　At　present,　the　urgent　need　are　failure　prediction　and　health　management　technology　of　aero-engine,　the　remaining　useful　life　(RUL)　prediction　is　one　of　the　core　technologies　of　health　management　technology.　In　this　paper,　we　propose　a　combined　model　for　aero-engine　life　prediction　which　based　on　Long　Short-Term　Memory　network　based　on　multi-headed　attention　mechanism　and　Autoregressive　Integrated　Moving　Average　Model.　Using　the　multi-headed　attention　mechanism　to　focus　on　the　information　of　the　input　sequence　related　to　the　current　time　step,　can　improve　the　level　of　attention　of　the　LSTM　neural　network　to　the　key　information　of　the　sequence　data,　By　extracting　important　time　features　from　different　moments,　and　using　ARIMA　time　series　model　to　extract　linear　features　advantage　will　be　more　accurate.　The　model　experiments　based　on　actual　flight　data　indicate　that　the　prediction　result　of　this　model　is　better　than　the　traditional　statistical　methods　and　machine　learning　methods,　so　it　is　effective　and　it　has　advantages　in　improving　the　accuracy　of　remaining　useful　life　prediction　of　aero-engine.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2025.