When a heterogeneous graph was constructed for graph neural network model for the whole corpus to text classification, it was difficult to generalize to new samples and word order information was missed. To solve these problems, a text classification that incorporated dual graph features and contextual semantic information was proposed. Firstly, a syntactic dependency graph and a co-occurrence graph were constructed for individual text to inductive learning for new samples. Text features were captured from two perspectives, which solved the problem of ignoring dependency information between words. Secondly, bi-directional long short-term memory network (BiLSTM) was used to encode text, which solved the problem of ignoring word order features and having difficulty in capturing contextual semantic information. Finally, dual graph features were fused to enhance the classification performance of graph neural network model. The experimental results on MR, Ohsumed, R8 and R52 datasets show that the model can extract richer text features and have an average increase of 2.17%, 5.38%, 0.61% and 2.48% in accuracy compared with classical text classification models.