Abstract: Aiming at the problems that the number of clusters cannot be dynamically changed and the text classification results are not accurate enough during the text clustering process,this paper introduces and improves the Growing Hierarchical Self-Organizing Map (GHSOM) algorithm to improve text clustering accuracy,and tries to use the improved GHSOM algorithm to build a knowledge map of civil aviation regulations.The GHSOM algorithm has a multi-level hierarchical structure,and each layer contains several independent growing SOMs.Through the growth of the scale,the data set is described in more detail to a certain extent,and the classification effect is improved.Based on this,taking various laws and regulations in the field of civil aviation as the sample data set,combined with Chinese word segmentation,keyword extraction,file vector and other technical means,the text is clustered and analyzed using the improved GHSOM algorithm,and finally the construction of civil aviation regulation knowledge map is completed.Experimental results show that the proposed algorithm has significant text clustering ability.The civil aviation regulation knowledge map constructed by this algorithm has achieved good classification results,and its evaluation indicators such as accuracy and recall rate have been further improved.

Key words: GHSOM, Knowledge map, Natural language processing, Text clustering, word2vec