As　a　powerful　tool　for　measuring　complexity　and　randomness,　multivariate　multi-scale　permutation　entropy　(MMPE)　has　been　widely　applied　to　the　feature　representation　and　extraction　of　multi-channel　signals.　However,　MMPE　still　has　some　intrinsic　shortcomings　that　exist　in　the　coarse-grained　procedure,　and　it　lacks　the　precise　estimation　of　entropy　value.　To　address　these　issues,　in　this　paper　a　novel　non-linear　dynamic　method　named　composite　multivariate　multi-scale　permutation　entropy　(CMMPE)　is　proposed,　for　optimizing　insufficient　coarse-grained　process　in　MMPE,　and　thus　to　avoid　the　loss　of　information.　The　simulated　signals　are　used　to　verify　the　validity　of　CMMPE　by　comparing　it　with　the　often-used　MMPE　method.　An　intelligent　fault　diagnosis　method　is　then　put　forward　on　the　basis　of　CMMPE,　Laplacian　score　(LS),　and　bat　optimization　algorithm-based　support　vector　machine　(BA-SVM).　Finally,　the　proposed　fault　diagnosis　method　is　utilized　to　analyze　the　test　data　of　rolling　bearings　and　is　then　compared　with　the　MMPE,　multivariate　multi-scale　multiscale　entropy　(MMFE),　and　multi-scale　permutation　entropy　(MPE)　based　fault　diagnosis　methods.　The　results　indicate　that　the　proposed　fault　diagnosis　method　of　rolling　bearing　can　achieve　effective　identification　of　fault　categories　and　is　superior　to　comparative　methods.