The operation and maintenance of nuclear power plant equipment generate massive amounts of heterogeneous data. Effectively archiving and utilizing lifecycle data of such equipment is a key challenge in the ongoing digital transformation of the nuclear industry. As a virtual replica of physical assets, the digital twin enables real-time integration of data across the design, manufacturing, operation, and maintenance stages, providing a comprehensive view of equipment performance and intelligent analytical capabilities. This study proposes a digital twin-based framework for lifecycle data management and intelligent analysis of key nuclear power plant equipment. The framework includes data archiving structure design, intelligent fault diagnosis, and maintenance optimization methods. By synthesizing data from authoritative public sources, the study compares traditional methods with digital twin-driven intelligent analysis in terms of fault diagnosis accuracy, equipment reliability, and maintenance efficiency. Results show that digital twin technology can significantly improve fault diagnosis accuracy (from approximately 90% to over 99%), reduce unplanned outages and failure rates (by about 70%), and substantially lower operation and maintenance costs. These findings provide a valuable reference for the digitalized maintenance of nuclear power equipment and contribute to enhancing the safety and economic efficiency of nuclear power plants. Finally, the study discusses the challenges in standardization, data quality, and security during digital twin implementation and outlines future research directions.