Abstract
From classicism to romanticism to the diverse development of today, music has become an inseparable part of human society. In fact, an artist will be affected by many factors when creating new music. It is undeniable that many songs have many similarities, and due to the mutual influence of artists, with the development of time, the music type has undergone a major change. In our Index Weight Analysis Model, we first analyzed the consistent relationship between music indicators and music influence. Then we use the extreme value method for dimensionless processing of the original data and calculate the correlation coefficient. In order to eliminate the impact of the resolution coefficient on the accuracy of our subsequent calculations, we use the distance between the two points to calculate the weight coefficient, and finally calculate the weighted correlation degree, which is Music Influence. In the end, we found the musicians with the highest musical influence among the 20 genres, and drew a directional network using a musician as an example. Also, we use the same method to analyze the contribution of each indicator to music influence and we find that the biggest contribution is Tempo.
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