Music Trend Prediction Based on Improved LSTM and Random Forest Algorithm

Abstract

As one of the entertainment consumption products, pop music attracts more and more people's attention. In the context of big data, many pop music listeners can determine the development trend of pop music to a large extent. In order to predict the trend of pop music, we can dig and analyze the audience's preferences and preferences deeply based on massive user data. This paper proposes a music trend prediction method based on improved LSTM and random forest algorithm. The algorithm first performs abnormal data processing and normalization processing on the test data set. Then the important features are selected by the random forest algorithm and corrected by the rough set compensation system. Finally, the prediction is made by improving LSTM. In the experiment, RMSE and MAER are used as the performance evaluation indexes of the algorithm, and the results show that the proposed algorithm can better predict the music popularity trend. At the same time, the root means square error and mean absolute error index are improved obviously.