Aiming at the problem that it is difficult to evaluate the overall health status of the crane rotary system under realtime conditions, a health evaluation method for the rotary system combining Laplacian Eigenmaps and KullbackLeibler distance is proposed. After collecting the multidimensional signal of rotary system, the Laplacian eigenmaps and Random Forest are used to reduce noise and dimensionality of the signal. Then combined with the working principle of the rotary system, the health performance of the rotary system is characterized by Gaussian kernel density estimation. The KullbackLeibler distance between different rotary system is calculated by probability density to characterize the health performance of the rotary system. The test results show that this method can avoid the noise interference of the original data and the health assessment results of the rotary system are consistent with the expert assessment results.