Research on Statistical Characteristics of Chinese Language Network and Semi-Supervised Document Clustering Algorithm

In the digital age, the explosion of information has made us surrounded by massive amounts of data every day. Especially in the Chinese network environment, the processing and analysis of text data has become particularly important. Chinese network computing features refer to the features of Chinese network texts extracted by computational methods, which can help us better understand and analyze large-scale Chinese document data. The semi-supervised document clustering algorithm is a clustering method that combines a small amount of labeled data and a large amount of unlabeled data, which can effectively improve the performance and efficiency of clustering. This paper will discuss in depth the combination of Chinese network meter features and semi-supervised document clustering algorithms, and how they work together to improve the effect of Chinese document clustering. First of all, we need to understand the characteristics of Chinese online texts. Chinese documents often contain rich information, such as emotions, themes, styles, etc., which are important clues for text analysis. However, the processing of Chinese text is more complicated than that of English because there are no obvious separators, such as spaces, between Chinese characters. In addition, linguistic phenomena such as synonyms, polysemous words, and idioms in Chinese make text analysis difficult. Therefore, accurate extraction of Chinese network computing features is the premise of effective clustering.