Abstract: In view of the current problems of low accuracy and poor generalization ability of the electric spindle rotation accuracy degradation model, a mechanism-data-driven rotation accuracy degradation model considering working conditions from the perspective of bearing wear is established. Firstly, a contact mechanics model of the bearing is established based on Hertz's contact theory, and the relationship between bearing wear and spindle accuracy degradation is described using Archard's wear theory. Thereby, a degradation mechanism model of the electric spindle rotational accuracy considering the effects of bearing wear is developed. Secondly, an experimental platform for the rotation accuracy of the electric spindle is built considering the working conditions, and long-term degradation experiments are conducted on the VF150-X1 electric spindle under different working conditions. Finally, a mechanism-data-driven electric spindle rotational accuracy degradation model is established by integrating the mechanism model with the measured data using a regression algorithm. The verification experiments show that the relative error between the actual degradation of the electric spindle and the predicted degradation of the model is 7.33% under the conditions of rotation speed of 2 000 r/min, load of 350 N and experimental time of 150 h, which demonstrates that the mechanism-data-driven electric spindle rotational accuracy degradation model constructed in this paper exhibits good predictive performance under specific operating conditions.