In order to effectively monitor the rolling bearing performance degradation trend and its abnormal fluctuations, a dynamic early warning method of rolling bearing performance degradation trend based on the relative energy entropy of variational mode decomposition (VMD) and the adaptive ARMA model is proposed. Methods VMD was used to decompose the life data of rolling bearing to obtain bandlimited intrinsic mode functions (BLIMFs). The energy of the BLIMFs component is analyzed by relative entropy, and the characteristics of rolling bearing performance degradation are extracted to obtain the bearing performance degradation evaluation index of VMD relative energy entropy. The energy entropy value extracted by VMD decomposition is used as an input for ARMA model for dynamic regression prediction. The test results show that this method can effectively monitor the degradation trend of rolling bearing performance and the abnormal fluctuation of indexes, and verify the effectiveness of the proposed method.