Ensuring the precise maintenance and stable operation of mineral processing equipment has always been an important challenge for mining-related enterprises while developing predictive maintenance systems for equipment has become a crucial means to reduce maintenance costs and improve production efficiency. This study analyzes the functional requirements of predictive maintenance systems, designs architecture and overall functional structure for a predictive maintenance system based on a micro-service architecture, and elaborates on the key technologies of the system. Moreover, the study proposes an evaluation model for equipment health status based on a multi-scale CNN fusion attention mechanism, as well as a prediction model for current trend fusion based on CNN and BiLSTM, to support the construction of the predictive maintenance system. The completed system has been applied at Ansteel Group Guanbaoshan Mining Co. Ltd., where the proposed model undergoes testing. The results show that the proposed model outperforms existing models with its high accuracy and robustness. The developed system can provide precise equipment maintenance plans, reduce equipment maintenance costs, and improve enterprise production efficiency.