Abstract
The purpose of this article is to establish an algorithm model that can measure the influence of music, capture the evaluation index reflecting the influence of music, and extend the model to other fields such as politics, culture, and society. We have established a music influence-oriented network algorithm model based on influencers and followers, where each artist is a node, and each follower is a connection between artists. We define relative interaction strength indicators to help understand the entire network algorithm. In addition, we also used time, genre and other scales to further optimize the network algorithm. We first use the PCA algorithm to determine indicators that reflect music similarity, such as vitality, activity, popularity, overall loudness, etc. On this basis, an evaluation algorithm model based on cosine similarity is established to calculate music similarity values of different genres. In addition, we use the K-MEANS algorithm to normalize each feature index and sum its variance. Finally, we noticed that the similarity of artists within genres is higher than the similarity of artists between genres. We further analyzed the differences and influences within and between genres. Taking time as a distinction, a relative heat map of the interactive influence of genres is drawn. It is understood that certain genres will obviously have a certain influence over time. We summarize this model as an impact correlation analysis model. First, we choose a representative influencer. Then, based on the cosine similarity, we obtained the music similarity with the fans in batches, thus more intuitively concluded that the Internet celebrities did affect the respective artists. In addition, we combined the calculation of SPSS variance and selected different indicators to visualize the radar chart to understand the attractiveness differences of certain music features. We first select the musical characteristics with obvious changing trends, then locate the position of the changer in the music evolution process through the time distribution diagram of the corresponding work, and finally select the representative changer. We analyzed the change history of each indicator in the selected genre over time, and finally got the global directed network diagram. Based on the network algorithm model established in the previous question, we analyzed the background of the times and found that there is an interaction between music and the cultural environment. Finally, we also analyzed the advantages and disadvantages of the algorithm model, and discussed the application of the method in other fields.
Cosine similarity cluster analysis model based effective power systems fault identification
