Aiming　at　the　two　key　links　of　rolling　bearing　feature　extraction　and　fault　identification,　a　fault　diagnosis　was　proposed　based　on　GCMWPE　and　parameter　optimization　SVM.　First,　the　GCMWPE　was　applied　to　comprehensively　characterize　rolling　bearing　fault　feature　information,　and　a　high-dimensional　fault　feature　set　was　constructed.　Then,　the　S-Isomap(isometric　mapping)　was　utilized　for　efficient　secondary　feature　extraction.　Finally,　BAS(beetle　antennae　search)-SVM　was　employed　to　diagnose　and　identify　fault　types.　The　proposed　method　was　applied　to　the　experimental　data　analysis　of　rolling　bearings,　and　the　results　show　that　the　feature　extraction　effect　of　GCMWPE　is　superior　than　that　of　multiscale　weighted　permutation　entropy,　composite　multiscale　weighted　permutation　entropy,　and　generalized　multiscale　weighted　permutation　entropy;　the　feature　extraction　method　combining　GCMWPE　and　S-Isomap　may　effectively　distinguish　different　fault　types　of　rolling　bearings　in　low-dimensional　space;　the　recognition　accuracy　and　recognition　speed　of　BAS-SVM　is　better　than　that　of　particle　swarm　optimization　SVM,　simulated　annealing　SVM　and　artificial　fish　swarm　algorithm　support　vector　machine;　the　proposed　method　may　effectively　and　accurately　identify　each　fault　types.　©　2021,　China　Mechanical　Engineering　Magazine　Office.　All　right　reserved.