Application Analysis of Big Data-Driven Algorithm Music Creation Generation System

In the field of music, music generation systems based on big data-driven algorithms have become an important research direction. This paper aims to explore the application of big data-driven algorithmic music generation systems in music creation and music education. Through the statistical analysis of the user’s operation record of the music, and the prediction of the artist’s music listening volume in the next stage, it can be judged that the artist’s music listening volume is the highest in a period of time, these artists, as music ambassadors of their respective eras, not only represent a sound, but are also leaders and promoters of music trends. In order to more accurately grasp and predict the dynamics of the music market, especially the popularity of specific artists, this paper proposes an innovative data analysis method that combines quadratic exponential smoothing, autoregressive moving average (ARIMA) model, and BP (backpropagation) neural network model. Quadratic exponential smoothing is a time series prediction technique that predicts future trends by smoothing historical data. This method is particularly effective in handling data with obvious trend and seasonal characteristics, and is also applicable for predicting the number of artist auditions in the music industry. It can help us capture long-term trends in the popularity of artists and provide a basis for prediction. This paper discusses the application prospect of a music generation system driven by big data algorithms in music creation and music education.