Graph　Convolutional　Networks　(GCNs)　have　recently　gained　significant　attention　in　the　field　of　graph　neural　networks　due　to　their　ability　to　learn　node　representations　and　handle　graph-structured　data.　In　this　paper,　we　propose　a　novel　GCN　architecture　that　combines　Inception　modules　and　residual　learning　to　enhance　the　expressive　power　and　efficiency　of　the　model.　The　proposed　architecture　is　composed　of　multiple　Inception　modules　with　residual　connections,　which　enable　the　network　to　learn　hierarchical　features　from　local　and　global　graph　neighborhoods.　To　further　improve　the　performance　of　the　model,　we　also　incorporate　skip　connections　that　allow　gradient　flow　across　multiple　layers.　We　evaluate　our　proposed　model　on　benchmark　datasets　and　demonstrate　its　superior　performance　compared　to　other　models.　Our　results　show　that　the　proposed　model　achieves　better　accuracy　and　convergence　speed　while　maintaining　a　low　computational　cost.　The　MAE　of　our　model　training　results　is　7.98　%　better　than　the　GCN　model　and　the　RMSE　is　21.6　%　better.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.