<p>Thin-walled ring-shaped structures are widely employed in aerospace applications and demand extremely high manufacturing precision. However, during machining, the combined effects of time-varying cutting forces and workpiece stiffness generate non-uniform deformation fields that are challenging to control. To address this, a hybrid mechanism-data digital twin, enhanced with spatial–temporal correlation, is developed for real-time reconstruction of machining deformation fields, achieving virtual-physical synchronization and to support in-process compensation and downstream positioning. Within this digital twin framework, the physical entity is the turning process of the thin-walled ring-shaped part; the virtual model comprises an elastic mechanistic model and a data-driven module. The elastic mechanistic model reconstructs the instantaneous deformation, and by incorporating a substructure method and temporal learning of cutting loads, the computational efficiency is significantly enhanced. Through spatial correlation learning of the deformation field, the global deformation field can be reconstructed using only a minimal number of sampling points. The data-driven module dynamically updates the mechanistic model using real-time cutting signals and historical deformation data, ensuring high-accuracy full-field reconstruction and virtual-physical synchronization. Compared with the traditional mechanistic model, the proposed method improves deformation field reconstruction accuracy by approximately 10.7% while substantially reducing computational load and the number of required sampling points. Furthermore, it enables directional decomposition of the deformation field, providing theoretical guidance and practical solutions for in-process compensation and downstream workpiece positioning.</p>