Traditional　similarity　life　prediction　methods　ignore　the　local　evolution　characteristics　of　degradation　process，which　leads　to　low　prediction　accuracy.　Traditional　characteristic　indexes　in　time　and　frequency　domain　are　difficult　to　realize　early　fault　monitoring，and　local　fluctuation　is　large　in　the　later　stage　of　degradation.　The　trend　fitting　strategy　of　Gaussian　function　is　introduced　and　an　improved　similarity　matching　optimization　method　is　proposed.　A　Jensen-Renyi　divergence　health　index　based　on　Gaussian　mixture　model　is　proposed　to　accurately　track　the　evolution　trend　of　rolling　bearing　degradation.　Since　it　is　difficult　to　obtain　a　large　number　of　degradation　signals　in　real　life　cycle，a　double　exponential　function　model　is　constructed　to　simulate　degradation　signals　and　verify　the　validity　of　the　simulation　data　to　expand　the　reference　dictionary　set.　Gaussian　function　is　used　to　fit　the　degradation　data　and　parameter　similarity　principle　is　proposed　to　predict　the　remaining　service　life.　The　experimental　results　of　rolling　bearing　life　cycle　degradation　verify　that　the　proposed　method　can　effectively　improve　the　prediction　accuracy　of　residual　life.　©　2023　Nanjing　University　of　Aeronautics　an　Astronautics.　All　rights　reserved.