The increasing demand for clean energy has made floating wind turbines (FWTs) a viable option. However, the operation and maintenance of FWTs face numerous challenges. The emergence of digital twins provides a potential solution. This study focuses on real-time monitoring and analysis techniques for FWTs, aiming to address key challenges in building the digital twin of such systems. We propose a monitoring and analysis framework for the digital twin of FWTs, covering three main aspects: ocean environment, FWT’s pose, and structural state. For the tower structure of FWTs, we present a state inversion method based on limited sensors, enabling rapid and comprehensive assessment of structural health. Based on this, we construct a scaled model of the FWT and apply the proposed methods to the monitoring and analysis of an actual FWT, realizing a complete process from data perception, storage, integration, and fusion to feedback control, creating an information closed-loop between the physical and virtual worlds. Furthermore, the application of the constructed digital twin platform validates the feasibility and effectiveness of the methods. By deeply integrating digital models, ocean environment data, and FWT state data, we create a comprehensive digital twin of the FWT, providing crucial support for transparency of state information, safety risk management, intelligent parameter adjustments, and informed decision-making.