Aiming　at　the　problem　that　the　composite　fault　vibration　signal　of　rolling　bearing　is　complex　and　it　is　difficult　to　effectively　extract　the　impact　characteristics　of　the　composite　fault,　a　composite　fault　diagnosis　method　of　rolling　bearing　based　on　multi-scale　fuzzy　entropy　feature　fusion　is　proposed.　Compared　with　traditional　fault　feature　extraction　methods　that　can　only　extract　single　fault　feature　information,　this　method　can　increase　the　discrimination　of　composite　fault　features,　effectively　separate　multiple　composite　fault　features,　and　more　comprehensively　characterize　composite　fault　feature　information.　First,　the　signal　is　processed　by　EEMD,　getting　a　series　of　IMF　components.　Secondly,　the　energy　and　kurtosis　index　of　the　IMF　component　are　calculated,　the　appropriate　IMF　component　is　selected　through　the　correlation　coefficient　to　obtain　a　new　time　series,　the　multi-scale　fuzzy　entropy　is　calculated,　and　feature　fusion　performed.　Finally,　the　least　square　support　vector　machine　is　used　to　diagnose　the　fault　of　the　fusion　feature.　The　method　is　verified　by　a　mechanical　failure　simulation　test　bench.　The　experimental　results　show　that　this　method　can　quantitatively　characterize　the　data　information　of　fault　signal,　improve　the　anti-interference　ability,　have　good　feature　extraction　ability　of　composite　fault　of　rolling　bearings,　and　can　effectively　identify　the　type　of　composite　fault.　Compared　with　the　method　using　multi-scale　fuzzy　entropy,　energy　and　kurtosis　index　alone,　the　accuracy　of　fault　diagnosis　increases　by　8.12　%　and　11.65　%,　respectively.