In order to accurately identify the different fault types of rolling bearings， a fault recognition method for rolling bearing based on quantum-behaved particle swarm optimization and multi-scale permutation entropy （QPSO-MPE） is proposed. Firstly， the original vibration signal of the rolling bearing is decomposed by ensemble empirical mode decomposition （EEMD）， and a series of intrinsic mode functions （IMFs） and a trend term are obtained. The IMF component containing the main fault feature information is selected by kurtosis as the metric to reconstruct the vibration signal. Then， the key parameters of MPE are optimized by the QPSO algorithm， and the multi-scale permutation entropy of the reconstructed signals are calculated by the optimized MPE model to construct the multi-scale permutation entropy feature set of bearing faults. Finally， the fault feature set is input to GG （Gath-Geva） fuzzy clustering algorithm for clustering recognition. The experiment results show that the QPSO-MPE based fault recognition method can accurately identify the typical faults of rolling bearings and verify the effectiveness of QPSO-MPE in fault feature extraction.