With the increasing complexity of equipment management, equipment digital twin technology, as an innovative management tool, is becoming an essential means to improve the efficiency of equipment lifecycle management. This paper proposes a credible self-evolution framework for digital twins, aiming to address the complexity and uncertainty faced by equipment in dynamic environments. The framework incorporates a self-evolution mechanism and a credibility assessment method to ensure the system’s stability and reliability in highly variable environments. Finally, experimental results demonstrate the effectiveness of the proposed approach in smart manufacturing and equipment management, proving its superiority.