A rolling bearing performance degradation assessment algorithm based on single ring theorem in the random matrix theory is proposed to solve the problem that the traditional feature extraction algorithm is not sufficient to extract the useful information of the bearing under the industrial big data environment, and the constructed index is difficult to effectively characterize the bearing degradation process. Firstly, through segmentation, randomization, amplification and dimensional reconstruction of the bearing sampling data, a high-dimensional random matrix of the bearing at the current moment is established; Secondly, given the good high-dimensional data processing ability of random matrix theory, the distribution of eigenvalues of bearing random matrix is investigated by using the single ring theorem. Finally, the average spectral radius and the distribution of eigenvalue discrete points are used as degradation indexes to describe the degradation history of rolling bearings. The application research of the proposed method was carried out through the IMS rolling bearing full life test data, the research results show that the two quantitative indexes established by using the average spectral radius and the number of discrete points proposed based on the single ring theorem, can effectively describe the degradation process of rolling bearings and are more sensitive to the early anomaly state of rolling bearing.