In　practical　application,　the　signals　measured　by　distributed　optical　fiber　vibration　sensing　system　are　mostly　non-stationary　random　signals,　and　the　key　to　realize　pattern　recognition　is　to　obtain　the　amplitude-time-frequency　instantaneous　characteristics　of　the　signals　accurately.　Existing　related　research　shows　that　Hilbert　transform　combined　with　empirical　mode　decomposition　can　obtain　the　instantaneous　energy　and　instantaneous　frequency　of　the　intrinsic　modal　component　of　measuring　signal.　The　subsequent　improved　ensemble　empirical　mode　decompostion　method,　has　pseudo　component　and　large　reconstruction　error,　while　complementary　ensemble　empirical　mode　decompostion　method　reduces　the　reconstruction　error,　but　increases　the　amount　of　computation,　which　cannot　guarantee　the　efficiency　and　accuracy　of　feature　extraction　and　classification.　In　this　paper,　the　feature　extraction　of　distributed　optical　fiber　vibration　sensing　system　was　realized　based　on　modified　ensemble　empirical　mode　decompostion　with　Hilbert　transform,　the　evaluation　mechanism　of　permutation　entropy　was　introduced　to　optimize　the　iteration　times　of　random　noise　in　the　decomposition　process.　Through　simulation　analysis　and　experimental　comparison,　it　was　verified　that　the　method　could　effectively　solve　the　problems　existing　in　the　above　methods　and　improve　the　system＇s　performance　in　processing　time　and　feature　accuracy.　Experimental　results　show　that　the　average　extraction　accuracy　of　the　proposed　method　for　single-frequency　vibration　signals　is　99.2%.　Compared　with　EMD　and　CEEMD,　the　average　feature　extraction　accuracy　of　mixed　vibration　signal　is　98.1%,　which　is　15.6%　and　7%　higher　than　EMD　and　CEEMD　respectively.　The　average　time　of　the　algorithm　is　the　shortest,　which　is　3.8259　s.　It　provides　a　reliable　and　efficient　method　for　signal　feature　extraction　of　distributed　fiber　vibration　sensing　system.　Copyright　©2021　Infrared　and　Laser　Engineering.　All　rights　reserved.