Research on the Multi-non-artistic presentation Form of Western Music History based on Weighted Function Algorithm

Abstract

Abstract Western music has an important influence on the development of modern music, and in the digital era, the artistic presentation form of Western music history needs to be processed and analyzed more intelligently. The study constructs a data mining model based on Western music history using hierarchical clustering and Quality Phrase mining to quantitatively and statistically analyze relevant studies of Western music history. The development of each music genre is explored by utilizing statistics on the number of musicians per genre and geographical distribution. Western music’s presentation can be explored by analyzing the number of sounds, types of sounds, and frequency of occurrence. During the 30 years from 1991 to 2020, the top three research volumes of Western music history in descending order are classical music history (259), music aesthetics (109), and modern music studies (100). The music genres with more than 300 musicians include Baroque, Classical, and Romantic, all of which have been developing for more than 200 years. From the 1600s to the 1960s, there was first an increase in the number of musicians in each genre and then a subsequent decrease in geographical distribution, with Classical being the most typical genre.