The modeling of reliability of the complex equipment has many problems, such as multi-source reliability data, extremely unbalanced data distribution, uncertain information, weak model interpretation ability, and high misjudgment rate. This paper integrates cyber and physical system, deep learning and interpretable artificial intelligence to build virtual and real integration architecture for the health monitoring of marine equipment, to convert human “knowledge” into actual model and embed into deep learning network, and then proposes a migration health diagnosis method of large marine equipment lifting system based on the interaction of virtual reality. In addition, to transform the health warning problem of marine equipment into reinforcement learning problem of the continuous interaction between intelligent warning system and marine equipment, to establish the deep reinforcement learning model of the end-to-end mapping of “health state-warning strategy” for the intelligent warning decision of marine equipment. Finally, taking the marine equipment lifting system as an example, the application method of the model proposed above is introduced and its validity is verified.