Prediction of online novel rankings based on BP neural network

Abstract:

In recent years, with the rise of the "IP" boom, the market of online literature is developing rapidly, has gradually become a popular type of entertainment investment industry. This paper introduces the machine learning method to the prediction of the novel rankings, obtains the network novel information through the web crawler, extracts the characteristics of the influence rankings, constructs the BP neural network model to predict the range of the novel rankings. In view of the non equilibrium of the prediction results, the ROC curves and AUC value used as the classification performance metrics, the accuracy is more accurate. Compared with the traditional literature qualitative analysis method, machine learning method is more predictable, interpretable and applicable.