Abstract: Given the nonlinear and non-smooth characteristics of rolling bearing vibration signals, which pose challenges for fault features extraction, this work introduces a diagnosis method that intgrating DMD and CMAE, characterizing with superior noise reduction capabilities and a more accurate entropy distribution law. The DMD method can decompose raw signals into single-frequency modal components, effectively capturing the signal characteristics and avoiding the signal nonlinearity and nonsmoothness. DMD was employed to decompose and reconstruct the original rolling bearing signals, achieving reconstructed signals to mitigate noise interference. CMAE method was then used to quantify the entropy value of the reconstructed signals. By comparing the entropy values of the rolling bearing under various operating conditions with its life curve, the results show that the entropy value curve presents much more obvious non-stationary characteristics with notably enhanced fault features. And the range of variation of CMAE entropy value was quantified at a scale factor of 40 when rolling bearing failure occurs.