Aiming at the problems of low reliability and sensitivity when evaluating the degradation of bearing performance with traditional characteristic indicators, a method for evaluating the degradation of rolling bearing performance based on dispersion entropy and cosine Euclidean distance is proposed. First, the vibration signal of the rolling bearing to be tested is divided into health data and test data, decomposed by EEMD respectively to obtain several Intrinsic Mode Functions (IMF). Calculatethe correlation coefficient between each IMF component and the original signal, and the IMF components are selected according to the correlation coefficient criterion to reconstruct signal. Then, the dispersion entropy of the reconstructed signal is calculated, and the Euclidean distance and the cosine distance are combined to obtain the degradation index cosine Euclidean distance between the health data and the test data dispersion entropy. Finally, the Chebyshev inequality is used to calculate the cosine Euclidean distance health threshold to evaluate the degradation of the bearing performance. The experimental result shows that the cosine Euclidean distance between dispersion entropy can effectively and timely judge the degradation state of the bearing performance, and compared with other indexes, its sensitivity and robustness are higher, which can better describe the degradation trend of the rolling bearing performance, and provide a new solution for the evaluation of the rolling bearing performance degradation.