Aiming at the problems of extensive classification of health status of S700K switch machine, slow diagnosis speed and low efficiency; a diagnosis method based on CEEMDAN and kernel based extreme learning machine (KELM) is proposed. Firstly, the power data of S700K switch machine is decomposed by adaptive noise complete set empirical mode decomposition, and six intrinsic mode functions (IMF) are obtained. Then, the fuzzy entropy (FE) value of the intrinsic mode function is calculated as the characteristic parameter to characterize the health state of the switch machine. Finally, the kernel limit learning machine improved by sparrow search algorithm (SSA) is used to diagnose nine health states, and compared with SVR and ELM models. The simulation results show that the accuracy rate and the recall rate of the improved kernel based extreme learning machine model are 97. 8%, 98. 0% and 97. 8% respectively. Compared with SVR and ELM models, SSA-KELM model improves the diagnostic accuracy rate by at least 2. 2% on the basis of ensuring the running speed.