This　study　presents　an　accurate　aircraft　landing　gear　load　estimation　model　leveraging　graph　convolutional　neural　networks　(GCN),　which　predicts　loads　from　structural　strain　distribution　data.　A　ground-based　experimental　system　is　established,　deploying　fiber　grating　strain　sensors　at　key　landing　gear　points　to　gather　strain　data　under　various　operating　conditions　for　model　training.　The　GCN　model　undergoes　strain-to-load　mapping　training　and　testing,　with　prediction　accuracy　and　stability　evaluated　using　maximum　relative　error,　average　relative　error,　and　standard　deviation.　Results　showcase　stable　and　precise　predictions,　with　X,　Y,　and　Z　load　predictions　achieving　maximum　relative　errors　of　5.18%,　4.15%,　and　3.57%,　respectively,　and　average　relative　errors　of　1.58%,　0.61%,　and　0.75%,　respectively,　alongside　low　standard　deviations　of　0.59N,　0.74N,　and　0.46N.　Comparative　analyses　against　multiple　linear　regression　and　advanced　neural　networks　(LSTM,　CNN,　MLP)　underscore　the　GCN　model＇s　superior　prediction　accuracy.　This　work　holds　significant　potential　for　applications　in　aircraft　structural　health　monitoring.　©　2024　IEEE.