Fusion of the word2vec word embedding model and cluster analysis for the communication of music intangible cultural heritage

This article aims to propose a method for computing the similarity between lengthy texts on intangible cultural heritage (ICH), to facilitate the swift and efficient acquisition of knowledge about it by the public and promote the dissemination and preservation of this culture. This proposed method builds on traditional text similarity techniques. The ultimate goal is to group together those lengthy texts on ICH that exhibit a high degree of similarity. First of all, the word2vec model is utilized to construct the feature word vector of music ICH communication. This includes the acquisition of long text data on music ICH, the word segmentation of music ICH communication based on the dictionary method in the field of ICH, and the creation of a word2vec model of music ICH communication. A clustering algorithm analyzes and categorizes ICH communication within the music. This procedure involves employing text semantic similarity, utilizing a similarity calculation method based on optimized Word Mover Distance (WMD), and designing long ICH communication clustering. The main objective of this analysis is to enhance the understanding and classification of the intricate nature of ICH within the musical realm. Finally, experiments are conducted to confirm the model's effectiveness. The results show that: (1) the text word vector training based on the word2vec model is highly accurate; (2) with the increase in K value, the effect of each category of intangible word vector is improving; (3) the final F1-measure value of the clustering experiment based on the optimized WMD is 0.84. These findings affirm the usefulness and accuracy of the proposed methodology.