A study of Tibetan text classification based on CBAt hybrid neural network model

Text classification is an important research work in the field of natural language processing, and the emergence of deep learning methods has greatly promoted the development of text classification techniques, which is also effective for Tibetan text classification. This paper proposes a CBAt hybrid neural network based Tibetan text classification model to classify 14,000 texts in Tibetan dataset, using the convolutional results in the traditional CNN model as the input of the BiLSTM model and introducing the Attention mechanism to increase the model's feature extraction of important information of Tibetan texts, so as to improve the classification accuracy. The paper also compares this dataset with traditional machine learning algorithms and a single neural network model, and the experimental results show that the improved hybrid neural network model proposed in this paper performs better in the classification of Tibetan texts.