French natural language processing based on machine learning algorithms

With the development of intelligent technology, optimizing the processing of natural language by computers has become one of the current research hotspots. In order to enable computers to effectively recognize natural languages such as French text, a machine learning algorithm with improved K-means clustering has been developed. The study designed a word extraction box for text content using the frequent pattern sequence tree method, which enables accurate word segmentation of text content by computers. At the same time, text based natural language becomes recognizable data content after text preprocessing. By improving the K-means clustering algorithm, the computer's ability to recognize French natural language text content can be enhanced, thus developing natural language processing technology based on French content. The research results show that the number of feature words for text clustering needs to be maintained between 20-35 to ensure the extraction of the text extraction algorithm. Meanwhile, the research method achieved an extraction accuracy range of 88.69%-92.73% for French text, which is 16.26%-31.59% higher than the extraction accuracy of other algorithms. The research algorithm shows significantly better text extraction performance for French natural language than other algorithms. Therefore, researching algorithms can provide a French language text processing technique with stronger text mining performance for computers.