Urban　metro　systems　are　integral　to　modern　public　transport,　making　it　essential　to　understand　the　factors　influencing　passenger　flow　for　effective　system　planning　and　operations.　Current　evaluation　methods　for　feature　importance　often　lack　precision,　creating　challenges　in　accurately　profiling　influential　factors.　Recent　advancements　in　explainable　artificial　intelligence　(XAI)　present　opportunities　to　enhance　feature　interpretability　and　refine　natural　feature　profiling　frameworks　for　metro　passenger　flow.　This　study　discusses　three　XAI　methods,　i.e.,　LOFO,　Fast-LOFO,　and　SHAP,　in　systematically　evaluating　feature　importance　in　metro　systems.　Utilising　the　metro　smartcard　records　from　Suzhou,　we　construct　a　hierarchical　tagging　system　for　natural　features.　Each　XAI　method　is　applied　to　assess　feature　importance　across　key　factors　like　time　of　travel,　weekday　status,　and　points　of　interest,　allowing　for　a　comparative　analysis　of　their　effects　on　passenger　flow.　Our　findings　show　that　while　dominant　features,　such　as　travel　hour　and　weekday　status,　consistently　rank　as　the　most　influential　across　methods,　variations　arise　in　the　treatment　of　secondary　features.　Tree-based　models　provided　stable,　high-level　rankings,　whereas　SHAP　offered　deeper,　localised　insights,　highlighting　how　specific　features　influence　individual　predictions.　These　differences　underscore　the　need　for　a　multi-method　approach　to　achieve　a　complete　and　context-sensitive　feature　profile.