Inter-cell　interference　poses　a　significant　challenge　to　the　performance　and　reliability　of　cellular　networks　due　to　the　complex　spatial　and　temporal　relationships　between　network　nodes.　Addressing　this　issue　requires　accurate　prediction　and　assessment　of　interference.　This　paper　presents　a　novel　solution　leveraging　the　strengths　of　a　weighted　graph　convolutional　network　(WGCN)　combined　with　graph　coloring　techniques.　Specifically,　we　propose　a　WGCN-based　interference　estimation　model　to　accurately　derive　the　real-time　inter-cell　interference.　Then,　a　graph　multi-coloring　problem　is　considered　for　the　interference　coordination.　To　address　the　color　collision　between　cells　and　the　color　(i.e.　spectrum　resources)　requirement　of　individual　cells　in　the　graph　coloring　problem,　we　propose　a　WGCN-assisted　graph　multi-coloring　(WGCN-GMC)　algorithm　to　allocate　spectrum　resources　rationally.　Simulation　results　demonstrate　that　our　approach　significantly　enhances　interference　coordination,　and　achieves　an　impressive　average　improvement　of　58.2　%　compared　to　the　traditional　GMC　algorithm　leading　to　improved　overall　network　performance.　©　2025　IEEE.