In order to improve the fault identification accuracy of bearing and simultaneously, reduce or even eliminate the influence of parameter discussion, a rolling bearing fault diagnosis method without parameter discussion based on bubble entropy is proposed in this paper. In this method, Variational Mode Decomposition based Refined Composite Multiscale Bubble Entropy (VMD-RCMBE) are combined together to extract fault features, MCFS is used to realize feature screening and dimensionality reduction, and Gorilla Troops Optimizer Optimized Kernel Extreme Learning Machine (GTO-KELM) is used to complete model training and pattern recognition. The effectiveness of the method proposed in this paper is proved by two bearing fault data sets. Through the comparative experiment with the existing methods, it is proved that this method can achieve higher classification accuracy though without the parameter discussion process, and the average detection accuracy for different data sets can reach more than 99.8%.