Based on the global search ability of quantum particle swarm optimization （QPSO） and the excellent time series classification ability of hidden Markov model （HMM）， a method of fault degree identification of rolling bearing is proposed， and the performance of the method is verified by the measured vibration signal. Firstly， the measured vibration signal is decomposed by the variable mode decomposition ， and the signal feature is extracted by the singular value decomposition. Then， the hidden Markov model is trained by QPSO algorithm and the sample signals， the trained hidden Markov model is used for bearing fault degree identification. Finally， the test signal is input into the model to identify the fault degree of rolling bearing. The results show that this algorithm can solve the problem of local optimization of parameters estimation of hidden Markov model， and can get high accuracy of fault degree identification of rolling bearing.