Considering the unique domain-specific information inherent in software requirement texts, as well as the important contextual relationships and inherent ambiguities they contain, this study proposes a model that integrates graph convolutional network (GCN) with BERT for automatic software requirements classification, named BERT-FGCN (BERT-FusionGCN). This model leverages the advantages of GCN in propagating information and aggregating features from neighboring nodes to capture the contextual relationships between words or sentences in requirement statements, thereby improving the classification results. Initially, a text co-occurrence graph and a dependency syntax graph of requirement texts are constructed. These graphs are then fused to capture the structural information of the sentences. The GCN is then employed to perform convolution on the graph structure of the modeled requirement statements to obtain graph vectors. Finally, these graph vectors are fused with the vectors obtained from BERT feature extraction to achieve automatic classification of software requirement texts. Experiments conducted on the PROMISE dataset demonstrate that BERT-FGCN achieves an F1-score of 95% in binary classification, and increases the F1-score by 2% in multi-class classification tasks.