Under the combined influence of environmental factors such as wind,waves and currents,floating offshore platforms undergo oscillatory motion in six degrees of freedom,posing a severe challenge to safety of offshore operations.Accurate short-term motion prediction can serve as an input condition to improve the performance of motion compensation devices.Additionally,it provides timely real-time warning information to guide safe operations.Deep learning algorithms involve models learning from existing data through extensive training to extract features and make predictions based on input data.This study leverages model trial data from various ocean platforms to establish a deep learning model based on Long Short-Term Memory(LSTM)networks.The model achieves accurate prediction of heave and surge motions within the next 20～40 s,with an overall accuracy exceeding 80%-90%.Sensitivity analysis explores the model's input/output window lengths and wave phase differences.The appropriate proportionality relationship between multiple platform mixed training is determined,extending the forecast time length based on this foundation and providing a recommended configuration reference for deep neural network models.
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