Graph　Convolutional　Network　(GCN)　is　extensively　used　in　text　classification　tasks　and　performs　well　in　the　process　of　the　non-euclidean　structure　data.　Usually,　GCN　is　implemented　with　the　spatial-based　method,　such　as　Graph　Attention　Network　(GAT).　However,　the　current　GCN-based　methods　still　lack　a　more　reasonable　mechanism　to　account　for　the　problems　of　contextual　dependency　and　lexical　polysemy.　Therefore,　an　improved　GCN　(IGCN)　is　proposed　to　address　the　above　problems,　which　introduces　the　Bidirectional　Long　Short-Term　Memory　(BiLSTM)　Network,　the　Part-of-Speech　(POS)　information,　and　the　dependency　relationship.　From　a　theoretical　point　of　view,　the　innovation　of　IGCN　is　generalizable　and　straightforward:　use　the　short-range　contextual　dependency　and　the　long-range　contextual　dependency　captured　by　the　dependency　relationship　together　to　address　the　problem　of　contextual　dependency　and　use　a　more　comprehensive　semantic　information　provided　by　the　BiLSTM　and　the　POS　information　to　address　the　problem　of　lexical　polysemy.　What　is　worth　mentioning,　the　dependency　relationship　is　daringly　transplanted　from　relation　extraction　tasks　to　text　classification　tasks　to　provide　the　graph　required　by　IGCN.　Experiments　on　three　benchmarking　datasets　show　that　IGCN　achieves　competitive　results　compared　with　the　other　seven　baseline　models.