For　remaining　useful　life　(RUL)　prediction　of　machinery,　model-driven　methods　often　use　a　single　model　to　process　individual　data,　which　is　difficult　to　adapt　to　the　diversity　of　degradation　behaviors.　Data-driven　methods　are　more　dependent　on　training　data,　and　in　practice　a　large　amount　of　run-to-failure　data　is　difficult　to　obtain.　In　this　paper,　a　new　hybrid　drive　of　data　and　model　method　is　proposed.　In　the　model-driven　path,　a　new　scalable　two-stage　linear/nonlinear　composite　model　is　constructed　to　represent　various　degradation　behaviors,　and　to　clarify　the　evolution　law　of　individual　degradation.　In　the　data-driven　path,　the　long　short-term　memory　prediction　network　is　trained　to　track　the　degradation　process　and　learn　knowledge　of　multi-sample　degradation　behavior.　The　newly　established　dynamic　matching　index　integrates　the　model-driven　and　data-driven　paths,　and　realizes　the　interactive　fusion　of　information　and　RUL　prediction　through　real-time　matching　of　hidden　layer　states.　The　whole　life　cycle　performance　degradation　data　of　two　sets　of　different　experimental　rigs　are　used　for　analysis,　and　compared　with　some　state-of-art　RUL　prediction　methods,　the　results　show　that　the　proposed　method　has　higher　prediction　accuracy.