Switch mode power supply (SMPS) is an important component of the electronic system, the fault state of SMPS has an adverse impact on the operation of the back-end components and the entire electronic system. Therefore, it is very necessary to identify the health state of SMPS. Under the environmental stresses, Multi-parameters of the components of SMPS will degrade. To effectively identify the state of SMPS, the paper presents the multi-parameter identification method based on the key features and Elman neural network. At first, the paper obtains the Wavelet Packet local energy features of the output. To improve the identification accuracy, the coefficient of variation are used to select the local energy features, the local energy features with lager coefficient of variation values were regarded as the key features. Finally, the relationship between the key features and parameters will be established based on Elman neural network. The results of the simulation and hardware experiments demonstrate that the proposed method can obtain the high identification accuracy and great practicability.