Mustard gas (HD) is a well-known chemical warfare agent, recognized for its extreme toxicity and severe hazards. Metal-organic frameworks (MOFs), with their unique structural properties, show significant potential for HD adsorption applications. Due to the extreme hazards of HD, most experimental studies focus on its simulants, but molecular simulation research on these simulants remains limited. Simulation analyses of simulants can uncover structure-performance relationships and enable experimental validation, optimizing methods, and improving material design and performance predictions. This study integrates molecular simulations, machine learning (ML), and molecular fingerprinting (MFs) to identify MOFs with high adsorption performance for the HD simulant diethyl sulfide (DES), followed by in-depth structural analysis and comparison. First, MOFs are categorized into Top, Middle, and Bottom materials based on their adsorption efficiency. Univariate analysis, machine learning, and molecular fingerprinting are then used to identify and compare the distinguishing features and fingerprints of each category. Univariate analysis helps identify the optimal structural ranges of Top and Bottom materials, providing a reference for initial material screening. Machine learning feature importance analysis, combined with SHAP methods, identifies the key features that most significantly influence model predictions across categories, offering valuable insights for future material design. Molecular fingerprint analysis reveals critical fingerprint combinations, showing that adsorption performance is optimized when features such as metal oxides, nitrogen-containing heterocycles, six-membered rings, and C=C double bonds co-exist. The integrated analysis using HTCS, ML, and MFs provides new perspectives for designing high-performance MOFs and demonstrates significant potential for developing materials for the adsorption of CWAs and their simulants.