Submarine landslides pose a serious threat to the safety of underwater engineering facilities. To evaluate the safety of undersea structures, it is important to estimate and analyze the sliding processes of potential submarine landslides. In this study, a convenient model for simulating submarine landslide processes is established by using Abaqus Eulerian large deformation technology with an explicit finite element framework. The VUSDFLD Fortran subroutine is used to consider the strain-softening and rate-dependency characteristics of soil shear strength. The proposed method is validated by comparing its results with experimental data and those of mainstream numerical methods. Then, the results of a dynamic analysis of typical potential submarine landslides in the Shenhu sea area are analyzed using the proposed method. Case studies are carried out under different soil shear strength distributions, and the influence of initial stress is also analyzed. The shear strain-softening and rate-dependency effects are highly involved in the runout process. The simulated landslide’s failure mode is consistent with the geophysical interpretation of existing landslide characteristics.

Ensuring the safe landing of deep-sea mining vehicles (DSMVs) on soft seabed sediments is critical for the stability and operational reliability of subsea mineral extraction. However, deep-sea sediments, particularly in polymetallic nodule regions, are characterized by low shear strength, high compressibility, and rate-dependent behavior, posing significant challenges for full-scale experimental investigation and predictive modeling. To address these limitations, this study develops a high-fidelity finite element simulation framework based on the Coupled Eulerian–Lagrangian (CEL) method to model the landing and penetration process of full-scale DSMVs under various geotechnical conditions. To overcome the high computational cost of FEM simulations, a data-driven surrogate model using the random forest algorithm is constructed to predict the normalized penetration depth based on key soil and operational parameters. The proposed hybrid FEM–ML approach enables efficient multiparameter analysis and provides actionable insights into the complex soil–structure interactions involved in DSMV landings. This methodology offers a practical foundation for engineering design, safety assessment, and descent planning in deep-sea mining operations.