In order to improve the accuracy and reliability of rolling bearing fault diagnosis in intelligent manufacturing mode, a fault diagnosis method based on Variational Mode Decomposition (VMD) and time-frequency domain entropy combined with improved Egret Swarm Algorithm (IESOA) to optimize BP neural network was proposed. Firstly, with the help of variational mode decomposition, the problem of pattern aliasing was successfully solved. Secondly, the time-domain Shannon entropy and frequency-domain spectral entropy of each modal component were extracted to construct fault feature vectors as input to the fault diagnosis model. Thirdly, the Halton sequence was introduced to initialize the egret population, the global optimization ability of the egret population optimization algorithm was enhanced, and the improved egret population algorithm was constructed to optimize the BP neural network (IESOA-BP), and finally the bearing dataset of Case Western Reserve University in United States was used for simulation. The results show that the entropy in the frequency domain of VMD time-added is more abundant in the characterization of fault characteristics. Compared with the traditional methods such as BP, PSO-BP, SSA-BP, ESOA-BP and SCESOA-BP, the IESOA-BP method shows higher classification accuracy and better stability in the fault diagnosis of rolling bearings.