In view of the problem that the early performance degradation point of rolling bearing is difficult to monitor, a method based on improved VMD and comprehensive characteristic indexes performance degradation assessment was proposed. Firstly, the parameters of VMD were optimized by Kullback Leibler divergence (KL-divergence), the bearing vibration signals were decomposed by the optimized VMD, and the modal components that sensitive to degradation characteristics were screened by wasserstein distance ( WD) method. Then singular value decomposition ( SVD) was performed to obtain singular value characteristics. Secondly, the comprehensive characteristic index of rolling bearing degradation was composed by combining the entropy energy rate ( EER) and confidence value (CV). Finally, SVDD model was used to calculate performance degradation index to realize early weak fault detection and performance degradation assessment. The validity of the proposed method was verified by using the bearing life cycle experiment data, the detection results of early performance degradation points are earlier than other degradation assessment methods, which provides a new idea for performance degradation assessment of rolling bearings.