The invention belongs to the technical field of bearing vibration signal identification, and discloses a rolling bearing fault diagnosis method and system, a storage medium, equipment and application,and the method comprises the steps: collecting original signals of a bearing in four states, carrying out the signal decomposition through VMD, and obtaining all IMF components; extracting signal features by using multi-scale permutation entropy, constructing a feature vector set, and dividing the feature vector set into a training sample and a test sample; initializing a whale algorithm population scale, an iteration frequency and an adaptive weight value; establishing an LSSVM model by using the initialization parameters; calculating a fitness value corresponding to each whale, and sortingthe whale according to the fitness; carrying out neighborhood search by adopting a von Noemann topological structure, carrying out information exchange in a neighborhood, finding an optimal whale in the neighborhood, and carrying out position updating according to a formula; and outputting the whale position with the optimal fitness as the parameter of the LSSVM for training, and carrying out fault classification on the test set. The method is better in fault classification performance and higher in accuracy.