Traditional　methods　for　detecting　urban　building　functional　types　using　high-resolution　remote　sensing　images　are　mainly　based　on　hand-designed　features,　ignoring　spatial　and　structural　features,　resulting　in　difficulties　in　recognizing　buildings　with　similar　shapes.　To　solve　this　problem,　this　study　constructs　a　multi-hop　graph　neural　network　(MHGNN)　that　integrates　high-level　structural　and　semantic　information　to　recognize　urban　building　functional　types　from　high-resolution　images.　Experiments　show　that　the　proposed　method　outperforms　traditional　graph　neural　building　classification　methods.　Our　proposed　method,　MHGNN,　demonstrates　superior　performance　compared　to　standard　GNNs　such　as　MLP,　GIN　and　GAT　in　multi-region　building　type　classification　tasks.　The　approach　achieves　state-of-the-art　metrics,　including　IoU　of　74.98%,　F1-Score　of　85.39%,　and　OA　of　89.56%-outperforming　baseline　methods　by　significant　margins.　©　2025　IEEE.