Aiming at the problems of few samples and difficult labeling in the remaining useful life prediction modeling of rolling bearings in high-speed and high-precision machining processes such as precision electronics and plastic shaping, this paper introduces the deep belief network that integrates the unsupervised and supervised fine-tuning learning methods to carry out the research on the prediction of the residual service life of rolling bearings. The vibration data features of rolling bearing are taken as input and the remaining useful life as output. The probability distribution of features accuracy quantified by energy function is taken as the basic component, and the feature output of the previous layer of the components is taken as the input of the next layer. The remaining useful life prediction model of rolling bearing is constructed by connecting multiple such components head to tail. The initial parameters of each unit in the model are obtained by unsupervised pre training of the original data, then the supervised fine-tuning of the model is carried out by using the remaining useful life label data to further improve the accuracy of the model prediction. The experimental results show that the method proposed in this paper can predict the remaining service life of rolling bearing. Compared with SVR and PCA-DBN, the prediction error is reduced by 28. 48% and 5. 57% respectively. This method has higher prediction accuracy in field prediction, and it can reduce the dependence on expert knowledge as well as improve generalization ability.