The　electric　arc　furnace　(EAF)　contributes　to　almost　one-third　of　the　global　iron　and　steel　industry,　and　its　harmonic　pollution　has　drawn　attention.　An　accurate　EAF　harmonic　model　is　essential　to　evaluate　the　harmonic　pollution　of　EAF.　In　this　paper,　a　data-driven　compartmental　modeling　method　(DCMM)　is　proposed　for　the　multi-mode　EAF　harmonic　model.　The　proposed　DCMM　considers　the　coupling　relationship　among　different　frequencies　of　harmonics　to　enhance　the　modeling　accuracy,　meanwhile,　the　dimensions　of　the　harmonic　dataset　are　reduced　to　improve　computational　efficiency.　Furthermore,　the　proposed　DCMM　is　applicable　to　establish　a　multi-mode　EAF　harmonic　model　by　dividing　the　multi-mode　EAF　harmonic　dataset　into　several　clusters　corresponding　to　the　different　modes　of　the　EAF　smelting　process.　The　performance　evaluation　results　show　that　the　proposed　DCMM　is　adaptive　in　terms　of　establishing　the　multi-mode　model,　even　if　the　data　volumes,　number　of　clusters,　and　sample　distribution　change　significantly.　Finally,　a　case　study　of　EAF　harmonic　data　is　conducted　to　establish　a　multi-mode　EAF　harmonic　model,　showing　that　the　proposed　DCMM　is　effective　and　accurate　in　EAF　modeling.