<p>IMFs components of ceemdan decomposition of roll.</p>

Accurate motion prediction of floating platforms is critical for ensuring operational safety in offshore engineering applications or marine equipment testing. However, the strong nonlinearity and non-stationary characteristics induced by complex marine environments pose significant challenges to conventional prediction models. This study proposes a reinforced hybrid neural network (CNN-BiLSTM-Attention) integrated with advanced signal processing techniques to address these challenges. The methodology combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for multi-scale signal analysis, coupled with temporal feature engineering through sliding window optimization. And the architecture innovatively integrates convolutional neural networks for spatial pattern extraction, bidirectional long short-term memory networks for temporal dependency modeling, and attention mechanisms for dynamic feature weighting. By analyzing datasets generated via hydrodynamic simulations, this study elucidates the model’s physical interpretability and establishes a closed-loop validation framework between data-driven methods and physics-based models. Finally, the predictive performance of the model is evaluated using motion datasets of the proportional platform in the water pool test under different working conditions, demonstrating its broad applicability and transferability by assessed using a dual-stage EWMA control line. Overall, the proposed CNN-BiLSTM-Attention model and its data-physics integrated validation method provide a reliable, interpretable and transferable solution for floating platform motion prediction, which can break through the limitations of single analysis methods, and provide a new research idea for integrating data-driven and physics-based methods in the field of ocean engineering.浮式平台运动的精准预测，对于海洋工程应用或海洋装备测试中的作业安全保障至关重要。然而，复杂海洋环境所引发的强非线性与非平稳特性，给传统预测模型带来了严峻挑战。本研究提出一种融合先进信号处理技术的增强型混合神经网络（CNN-BiLSTM-Attention），以应对上述挑战。该方法结合自适应噪声完备集合经验模态分解（CEEMDAN）开展多尺度信号分析，并通过滑动窗口优化实现时序特征工程。其架构创新性地融合了用于空间模式提取的卷积神经网络（CNN）、用于时序依赖关系建模的双向长短期记忆网络（BiLSTM），以及用于动态特征加权的注意力机制（Attention）。本研究通过分析水动力模拟生成的数据集，阐明了该模型的物理解释性，并构建了数据驱动方法与基于物理模型之间的闭环验证框架。最后，本研究采用不同工况下水池试验中缩比平台的运动数据集，对模型的预测性能进行评估；并通过双阶段指数加权移动平均（EWMA）控制线开展验证，证明了模型具备优异的普适性与可迁移性。综上，本文提出的CNN-BiLSTM-Attention模型及其数据-物理融合验证方法，可为浮式平台运动预测提供一套可靠、可解释且可迁移的解决方案，能够突破单一分析方法的局限，同时为海洋工程领域中数据驱动方法与基于物理模型的融合研究提供全新思路。