The scale of offshore wind turbines (OWTs) has increased in order to enhance their energy generation. However, strong aero/hydrodynamic loads can degrade the dynamic characteristics of OWTs because they are installed on soft seabeds. This degradation can shorten the structural life of the system; repetitive loads lead to seabed softening, reducing the natural frequency of the structure close to the excitation frequency. Most of the previous studies on degradation trained prediction algorithms with actual sensor signals. However, there are no actual sensor data on the dynamic response of OWTs over their lifespan (approximately 20 years). In order to address this data issue, this study proposes a new prediction platform combining a dynamic OWT model and a neural network-based degradation prediction model. Specifically, a virtual dynamic response was generated using a three-dimensional OWT and a seabed finite element model. Then, the LSTM model was trained to predict the natural frequency degradation using the dynamic response as the model input. The results show that the developed model can accurately predict natural frequencies over the next several years using past and present accelerations and strains. In practice, this LSTM model could be used to predict future natural frequencies using the dynamic response of the structure, which can be measured using actual sensors (accelerometers and strain gauges).