Measuring displacement response is essential in the field of structural health monitoring and seismic engineering. Numerical integration of the acceleration signal is a common measurement method of displacement data. However, due to the circumstances of ground tilt, low-frequency noise caused by instruments, hysteresis of the transducer, etc., it would generate a baseline drift phenomenon in acceleration integration, failing to obtain an actual displacement response. The improved traditional baseline correction methods still have some problems, such as high baseline correction error, poor adaptability, and narrow application scope. This paper proposes a deep neural network model based on empirical mode decomposition (EMD–DNN) to solve baseline correction by removing the drifting trend. The feature of multiple time sequences that EMD obtains is extracted via DNN, achieving the real displacement time history of prediction. In order to verify the effectiveness of the proposed method, two natural waves (EL centro wave, Taft wave) and one Artificial wave are selected to test in a shaking table test. Comparing the traditional methods such as the least squares method, EMD, and DNN method, EMD–DNN has the best baseline correction effect in terms of the evaluation indexes: Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and degree of fit (R-Square).

Wind–wave misalignment is a pervasive environmental phenomenon that significantly affects the structural integrity of floating offshore wind turbines (FOWTs). For a Spar-type FOWT across the full 0°–90° misalignment range, this study systematically conducts dynamic response characterization and parameter sensitivity analysis, quantifying the directional modulation effects on five critical dynamic indicators, including tower-base Fore-Aft (F-A) and side-to-side (S-S) bending moments, maximum Von Mises stress, and fairlead tensions. Results demonstrate that wind–wave misalignment triggers a significant redistribution of structural energy, where side-to-side bending moments and fairlead tensions exhibit distinct peak characteristics at specific non-collinear headings. Rather than merely evaluating structural responses, this study emphasizes the sensitivity of environmental parameters to reveal a dominance-switching mechanism. As the misalignment angle increases, the governing factors of structural response dynamically shift from wind variables to wave variables. This research provides a rigorous mechanical explanation for complex response evolution and offers a scientific basis for the robust design of floating wind turbines in multi-directional sea states.