Degradation prediction is an important technical approach for equipment health management. In recent years, a large number of time series prediction methods have been applied in degradation prediction. However, due to the complex structure and diverse functions of many large equipment, there are obvious stages in the degradation process, and the application of a single model to predict the degradation at different stages will significantly reduce the accuracy, and the retraining of the model for different stages will also bring the loss of time and computing power. To solve the problem of multi-stage degradation, this paper introduced the idea of transfer learning and proposed a multi-stage degradation prediction method combining degradation pattern recognition and LSTM-fine-tune. The LSTM model was trained with degradation data, and then part of network parameters was frozen. After identifying the new degradation stage of equipment, the model is fine-tuned with the degraded data of the new stage to quickly match the data of different stages. In order to verify the validity of the model, this paper takes oxygen concentrator as an example to apply the model. The results show that the proposed method can effectively identify the degradation of oxygen concentrator at three stages, and the mean square error of prediction for each stage is 0. 507, 8. 976 and 0. 375 respectively, which is far lower than the mean square error of direct prediction without segmentation of 76. 87. In terms of training time, compared with the retraining time of each stage, the training accuracy is obviously superior to the traditional methods such as Wiener process and Lstar.