A real-time structural safety assessment method based on digital twin technology is proposed to ensure the safe and stable operation of the environmental monitoring platform of the sea ranch during its service period. A three-level digital twin architecture is adopted to achieve rapid prediction and real-time visualization of the overall stress distribution state of the monitoring platform. The maximum error is less than 10%, which verifies the reliability of the simulation model; the structural stress field response database covering the monitoring platform under the common sea conditions during the service period is established by batch front simulation calculation of multiple working conditions; the structural stress field response database covering the monitoring platform under the common sea conditions during the service period is established by batch front simulation calculation of multiple working conditions;   the structural stress field response database covering the monitoring platform under the common sea conditions during the service period is established by batch front simulation calculation of multiple working conditions; under the simultaneous change of environmental parameters, the structural stress distribution of the monitoring platform can be predicted and visualized in real time.   In the case of simultaneous changes of environmental parameters, a fast prediction based on the structural response database is carried out by the improved inverse distance weight interpolation (IIDW) method, and the results show that the average absolute errors between the interpolated data and the simulation data for axial forces, moments, and spatial displacements at the monitoring points are 7.62%, 11.93%, and 5.77%, respectively. The average absolute errors between interpolation data and simulation data for all 2462 structural rods were 6.24%, 7.88% and 5.39%, respectively. The rapid structural safety assessment method of the ocean ranch environmental monitoring platform proposed in this study provides a feasible solution for the real-time monitoring of the overall stress and safety early warning during the platform's service period.