Research on Tibetan Text Categorization Based on CBAt Hybrid Neural Network Model
ZHU Yulei
QUN Nuo
YU Tao
YIN Zonghe
YONG Cuo
Nyima Tashi
Abstract：To improve the classification effect of deep learning models on Tibetan text classification,a CBAt hybrid neural net-work-based Tibetan text classification model is proposed to classify 14 000 Tibetan texts in the dataset.In this study,the convolu-tional results of the traditional CNN model are directly used as the input of the BiLSTM model,and an Attention mechanism is intro-duced to increase the feature extraction of important information of Tibetan texts,so as to improve the classification accuracy.The ar-ticle also compares this dataset with traditional machine learning algorithms,single and hybrid neural network models,and the ex-perimental results show that the improved hybrid neural network model proposed in this study performs better in classifying Tibetan text.
Keywords：deep learningTibetan text classificationCNNBiLSTMAttention mechanism
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：9( 3461-3469 )