The　E-shaped　cold-formed　steel　with　web　opening　(ESCFSWWO)　is　a　critical　component　in　modern　structural　systems,　and　accurate　calculations　of　its　axial　compressive　capacity　are　essential　for　ensuring　the　overall　stability　and　safety　of　structures.　However,　traditional　finite　element　analysis　(FEA)　and　experimental　methods　are　often　inefficient　and　require　complex　modeling　techniques　for　capacity　calculations.　Moreover,　the　interaction　between　section　design　parameters　and　structural　capacity　remains　unclear.　This　study　integrates　FEA,　experimental　methods,　and　machine　learning　to　propose　a　novel　approach　for　predicting　capacity　and　analyzing　parameter　interpretability.　A　dataset　comprising　1000　numerical　simulation　results　and　260　experimental　data　points　was　established.　Machine　learning　techniques,　including　BP　(Backpropagation)　neural　networks,　RBF　(Radial　Basis　Function)　neural　networks,　Decision　trees,　Random　Forests,　and　XGBoost　algorithms,　were　employed　to　develop　predictive　models　for　capacity.　The　SHapley　Additive　exPlanation　(SHAP)　method　was　utilized　for　interpretability　analysis.　Results　indicate　a　good　correlation　between　FEA　and　experimental　outcomes,　effectively　simulating　the　mechanical　behavior　of　the　components.　Comparative　analysis　reveals　that　the　XGBoost　model　demonstrates　the　highest　predictive　accuracy　and　generalization　capability.　SHAP　analysis　elucidates　the　influence　of　various　input　parameters　on　axial　compressive　capacity　predictions　and　clarifies　the　interactions　among　these　parameters.　This　research　provides　a　novel　reference　for　calculating　the　axial　compressive　capacity　of　ESCFSWWO,　and　the　proposed　methodologies　can　be　extended　to　other　similar　components.