As the supporting structure for the nacelle and rotor of a wind turbine, the tower plays a crucial role in ensuring safe and stable operation of the entire system. Accurate monitoring of tower structural deformation is essential for maintaining structural integrity and extending its operational lifespan. This paper proposes a digital twin-based data-physics fusion monitoring method for wind turbine towers under varying operating conditions. A 3D virtual model of the prototype wind turbine is developed to represent its physical counterparts, while a high-fidelity finite element model (FEM) is constructed to stimulate the structural behavior of the tower under wind loading. To enable efficient real-time analysis, a support vector regression (SVR) model is trained from FEM simulation data, allowing for a rapid representation of tower deformation. Additionally, a LSTM deformation prediction model is developed using sensor data to enhance monitoring accuracy. A digital twin framework and platform is established for real-time condition monitoring of wind turbine, integrating operating data from the wind turbine with tower deformation mechanism. Finally, experiments conducted on a small-scale prototype wind turbine demonstrate the effectiveness of the proposed methodology. This study provides a promising approach to integrating data, physics, and machine learning within a digital twin framework to enhance the accuracy of real-time condition monitoring for wind turbines.