Tibetan Web Text Clustering Based on Contrast Learning

In the field of natural language processing, text clustering, as a classical data analysis method, can be used to classify and analyze text well. This paper describes a novel clustering algorithm based on contrast learning, and carries out feature extraction, data enhancement and contrast clustering for Tibetan web hypertext data. This paper mainly introduces the data enhancement method based on pre-trained language model, and then uses contrast learning method to carry out deep clustering, and compares the traditional clustering algorithm. The feasibility of this method is proved by experimental evaluation. In the experiment, a variety of indicators are used to evaluate the algorithm, including clustering accuracy, normalized mutual information (NMI) and other indicators. The experimental results show that compared with traditional clustering algorithms, the proposed comparative clustering algorithm has improved in various indicators and has high accuracy.