Exploring the online interaction model of college English based on deep learning network

In this paper, we apply a deep learning model to discriminate sentiment in an interactive model of online college English education and propose a fusion model that splices convolutional neural networks and bidirectional long- and short-term memory neural networks horizontally. Convolutional neural networks are good at capturing the sentiment feature vectors using multi-channel convolutional kernels but are unable to extract the sentiment information above and below the sentiment sequence. The short and long-term memory neural network is able to extract the sentiment feature vectors by using recurrent neural networks, which can better compensate for the shortcomings of the convolutional neural networks. The online teaching of college English is selected as the object of analysis, and the improved Flanders interaction analysis system is used to study the online interaction process of college English so as to propose suggestions for the interaction of online teaching of college English. Then the performance of the model is analyzed through simulation experiments. Compared with the traditional TextCNN and BiLSTM, the CNN −BiLSTMATT sentiment analysis model has an accuracy of 0.8611, precision of 0.8471, recall of 0.8691, and F1 of 0.8562, so the CNN − BiLSTMATT sentiment analysis model is more suitable for college English online interaction. This study overcomes the disadvantages of online interaction and continuously improves the efficiency of online teaching interaction.