The digital twin coal preparation plant is a potentially effective way to realize the intelligent interconnection and interactive integration of the manufacturing physical world and the information world. Aiming at the difficulty in predicting and maintaining the state of the shearer in a harsh working environment, combined with the high-fidelity behaviour simulation characteristics of the digital twin and the powerful data mining capabilities of deep learning, a coal shearer health prediction driven by the integration of the digital twin and deep learning is proposed. Method. The article builds an information management and integration platform composed of a real-time database and a comprehensive information platform, and realizes the unified management of data integration and application systems.