Automatic classification of interactive texts in online collaborative discussion based on multi-feature fusion

The recognition of learners' speech intention in the online collaborative learning scene is of great significance for exploring the rules of knowledge construction such as knowledge development and emotional communication in the collaborative process. The essence of speech intention recognition is text classification. At present, text classification is mostly based on deep learning model. However, online collaborative discussion has the characteristics of strong contextual se-mantic relationship and key characteristic words. When only the deep learning method is used for text classification, there may be insufficient acquisition of contextual semantic relations and neglect of key feature words, resulting in a decline in the accuracy of classification results. Therefore, this paper proposes a multi-feature fusion model, which uses BERT to represent the text as a word vector, BiLSTM to extract the context features of the text, CNN to extract the local features of the text, and average pooling model to extract the average representation features of the text. The results show that the overall classification accuracy of the multi-feature fusion model is 84.30%.