The　rolling　bearing　vibration　signals　are　complex,　non-linear,　and　non-stationary,　it　is　difficult　to　extract　the　sensitive　features　and　diagnose　faults　by　conventional　signal　processing　methods.　This　paper　focuses　on　the　sensitive　features　extraction　and　pattern　recognition　for　rolling　bearing　fault　diagnosis　and　proposes　a　novel　intelligent　fault-diagnosis　method　based　on　generalized　composite　multiscale　weighted　permutation　entropy　(GCMWPE),　supervised　Isomap　(S-Iso),　and　marine　predators　algorithm-based　support　vector　machine　(MPA-SVM).　Firstly,　a　novel　non-linear　technology　named　GCMWPE　was　presented,　allowing　the　extraction　of　bearing　features　from　multiple　scales　and　enabling　the　construction　of　a　high-dimensional　feature　set.　The　GCMWPE　uses　the　generalized　composite　coarse-grained　structure　to　overcome　the　shortcomings　of　the　original　structure　in　multiscale　weighted　permutation　entropy　and　obtain　more　stable　entropy　values.　Subsequently,　the　S-Iso　algorithm　was　introduced　to　obtain　the　main　features　and　reduce　the　GCMWPE　set　dimensionality.　Finally,　a　combination　of　GCMWPE　and　S-Iso　set　was　input　to　the　MPA-SVM　for　diagnosis　and　identification.　The　marine　predators　algorithm　(MPA)　was　used　to　obtain　the　optimal　SVM　parameters.　The　effectiveness　of　the　proposed　fault　diagnosis　method　was　confirmed　through　two　bearing　fault　diagnosis　experiments.　The　results　have　shown　that　the　proposed　method　can　be　used　to　correctly　diagnose　bearing　states　with　high　diagnostic　accuracy.　(C)　2021　ISA.　Published　by　Elsevier　Ltd.　All　rights　reserved.