Abstract: With the rapid growth of software quantity and types,effectively mine the textual features of software requirements and classify the textual features of software functional requirements becomes a major challenge in the field of software enginee-ring.The classification of software functional requirements provides a reliable guarantee for the whole software development process and reduces the potential risks and negative effects in the requirements analysis stage.However,the validity of software requirement analysis is limited by the high dispersion,high noise and sparse data of software requirement text.In this paper,a two-layer lexical graph convolutional network model(TVGCCN) is proposed to model the graph of software requirement text innovatively,build the graph neural network of software requirement,and effectively capture the knowledge edge of words and the relationship between words and text.A deep integrated learning model is proposed,which integrates several deep learning classification models to classify software requirement text.In experiments of data set Wiodows_A and data Wiodows_B,the accuracy of deep ensemble learning model integrating Bert and graph convolution reaches 96.73% and 95.60% respectively,which is ob-viously better than that of other text classification models.It is fully proved that the deep ensemble learning model integrating Bert and graph convolution can effectively distinguish the functional characteristics of software requirement text and improve the accuracy of software requirement text classification.

Key words: BERT, Ensemble learning, GCN, Software requirements, Text classification, Text features