Abstract: To address the problem of difficulty in separating and extracting fault characteristics in weak fault conditions of rolling bearings, a cascaded feature enhancement method based on "VMD-MOMEDA" is proposed. With information entropy, envelope entropy, permutation entropy and sample entropy as independent optimization objectives, the golden jackal optimization algorithm is used to optimize the key parameters (K, Alpha) of VMD separately, obtaining four sets of optimal parameters and decomposing the same signal four times to obtain the IMF set. To screen out effective components, a comprehensive scoring index integrating "multiple entropy + kurtosis" is constructed. Within each IMF set, they are sorted and the top-4 components are selected. Subsequently, secondary MOMEDA deconvolution enhancement is performed on the selected components: firstly, the entropy-level fusion signal is obtained by enhancing with MOMEDA-1 and superimposing within the group, then the entropy-level fusion signals are enhanced with MOMEDA-2; finally, the four entropy-level enhanced signals are averaged in a secondary arithmetic operation to obtain the final enhanced signal, and the fault feature frequency and its harmonics are extracted from the envelope spectrum for diagnosis. Experimental results show that this method can significantly enhance the periodic impact components, suppress background noise, and make the fault feature frequency and its harmonics more prominent in the envelope spectrum.