In　spite　of　the　promising　prospects　of　the　WTaVCr　refractory　high-entropy　alloy　(RHEA),　the　nanoscale　structureproperty　relationship　remains　largely　unexplored.　This　study　introduces　a　supervised　machine　learning　(ML)　framework　to　develop　a　charge-transfer　ionic　potential　(CTIP)　for　the　W/Ta/V/Cr/O　multi-component　system.　This　novel　approach　demonstrates　exceptional　advantages　in　optimizing　numerous　parameters　of　a　highly　flexible　yet　physically　rigorous　potential,　leveraging　a　modified　distributed　breeder　genetic　algorithm　(DBGA)　to　balance　search　comprehensiveness　and　efficiency.　The　robustness　of　developed　potential　is　verified　through　cross-validation　against　first-principles　predictions　on　various　properties　of　metals,　alloys　and　oxides.　Subsequently,　dynamic　simulations　of　annealing,　mechanical　loading,　surface　oxidation　and　radiation　collision　are　conducted　utilizing　CTIP.　Results　of　these　simulations　align　with　previous　experiments　about　nanoscale　chemical　ordering,　plastic　deformation　behavior,　oxidation　mechanisms　and　radiation　tolerance.　These　findings　not　only　further　corroborate　the　reliability　of　CTIP　potential,　but　also　uncover　atomic-scale　insights　that　are　experimentally　unattainable,　thereby　enhancing　the　understanding　of　the　nanostructure-property　relationship.