Aiming at the problem that the health status of bearings cannot be directly monitored and predicted, we designed a L1 regularized bidirectional gating recurrent unit model and a health index constructed by Bray Curtis distance, which can directly represent the health status of bearings. Firstly, L1 regularization is used to extract effective features from the current bearing vibration data as degradation features, and the features of the first time window of the vibration data as health features. Then, the Bray Curtis distance between the bearing degradation features and health features is calculated to construct the HI of the bearing. The health status of the bearing is monitored in real time through the cloud monitoring platform, and the future health status is predicted using the BiGRU model. Once the HI of the bearing exceeds the change rate threshold, the platform will alarm immediately, and the health status of the bearing is predicted. The model is compared with bidirectional long short term memory and bidirectional long short term memory with attention models. The results show that the accuracy of this model is 97.52%, much higher than the other two models, and the model is more lightweight, which reflects the practicability of this method.