The　traditional　empirical　wavelet　transform　(EWT)　based　on　the　Meyer　wavelet　and　scale-space　method　can　decompose　a　signal　into　several　empirical　modes.　However,　this　method　is　not　effective　in　dealing　with　strong　noise　and　non-stationary　signals,　which　may　lead　to　modal　mixing　or　even　decompose　too　many　invalid　components.　For　this　purpose,　a　method　based　on　the　combination　of　enhanced　empirical　wavelet　transform　(EEWT)　and　correlation　kurtosis　(CK)　is　proposed　in　this　paper.　Firstly,　the　EEWT　is　used　to　segment　the　spectrum　based　on　the　characteristics　of　the　spectrum　fluctuations.　It　uses　the　minimum　points　of　the　envelope　as　the　boundaries　of　the　segmented　spectrum.　Secondly,　a　filter　bank　is　constructed　based　on　these　boundaries　and　a　maximum　value　order　statistics　filter　segments　the　Fourier　spectrum　with　the　adaptive　decomposition　of　the　signals.　Finally,　the　envelope　spectrum　generated　by　CK　is　used　to　screen　the　bearing　fault　information,　which　belongs　to　the　decomposition　of　a　signal　into　empirical　modes,　so　that　the　rolling　bearing　fault　can　be　accurately　diagnosed.　The　method＇s　effectiveness　is　verified　by　simulated　signal　experiments　and　rolling　bearing　fault　signals.　The　results　show　that　the　performance　of　the　proposed　method　in　this　paper　is　better　than　that　of　the　traditional　EWT.　Therefore,　the　method　can　be　applied　to　the　field　of　bearing　faults　or　other　mechanical　fault　diagnosis　directions.