Abstract: For the problem of identification and detection of rotor imbalance, an identification method of rotor imbalance based on multikernel learning support vector machine is proposed. Firstly, the influence of rotor imbalance is analyzed, and a signal decomposition and reconstruction method based on VMD is proposed. Secondly, a feature extraction method based on fuzzy entropy is proposed, which uses Gaussian kernel function as fuzzy function. The method has better noise robustness and lower data length dependence. Thirdly, an identification method based on multi-kernel learning support vector machine is proposed. Kernel functions of different features and scales are fused to form the kernel function library, and the optimal kernel function is selected. Finally, a cross-validation database is established to verify the proposed method in the simulation of different turbulence intensities. The accuracy is above 98%. The results show that the proposed method can identify rotor imbalance effectively.