There is an “under-learning” problem in the feature sensitivity of feature evaluation method for a single measure model. The support vector machine (SVM) parameter optimization algorithm has the disadvantages of slow convergence rate and easy to fall into the local extreme. Rolling bearing fault diagnosis of SVM based on improved quantum genetic algorithm method is proposed. Firstly, the characteristics of time domain, frequency domain constitute multi-domain original fault feature set. Secondly, a weighted model feature evaluation model based on correlation, distance and information is constructed. Finally, the weighted fault feature set is taken as input, and the quantum entropy is introduced into the improved quantum genetic algorithm (IQGA) to optimize the structural parameters of SVM. The intelligent identification of rolling bearing failure mode is completed. The experimental results show that compared with the classical quantum genetic algorithm (CQGA) and genetic algorithm (GA), the proposed method can quickly converge to the global optimal solution and ensure the clustering performance, and improve the diagnostic accuracy of rolling bearing.