Graph　Convolutional　Neural　Network　(GCN)　is　widely　used　in　text　classification　tasks.　Furthermore,　it　has　been　effectively　used　to　accomplish　tasks　that　are　thought　to　have　a　rich　relational　structure.　However,　due　to　the　sparse　adjacency　matrix　constructed　by　GCN,　GCN　cannot　make　full　use　of　context-dependent　information　in　text　classification,　and　it　is　not　good　at　capturing　local　information.　The　Bidirectional　Encoder　Representation　from　Transformers　(BERT)　has　the　ability　to　capture　contextual　information　in　sentences　or　documents,　but　it　is　limited　in　capturing　global　(the　corpus)　information　about　vocabulary　in　a　language,　which　is　the　advantage　of　GCN.　Therefore,　this　paper　proposes　an　improved　model　to　solve　the　above　problems.　The　original　GCN　uses　word　co-occurrence　relationships　to　build　text　graphs.　Word　connections　are　not　abundant　enough　and　cannot　capture　context　dependencies　well,　so　we　introduce　a　semantic　dictionary　and　dependencies.　While　the　model　enhances　the　ability　to　capture　contextual　dependencies,　it　lacks　the　ability　to　capture　sequences.　Therefore,　we　introduced　BERT　and　Bi-directional　Long　Short-Term　Memory　(BiLSTM)　Network　to　perform　deeper　learning　on　the　features　of　text,　thereby　improving　the　classification　effect　of　the　model.　The　experimental　results　show　that　our　model　is　more　effective　than　previous　research　reports　on　four　text　classification　datasets.