Prediction of online consumers’ buying behavior based on LSTM-RF model

Nowadays, in the era of big data, consumers generate tens of thousands of data on e-commerce platforms every day. How to use the massive hidden feedback information generated by network consumers on e-commerce platforms to predict consumers' purchasing behavior, thus improving the purchasing conversion rate of e-commerce enterprises is of great significance to enterprises. Therefore, aiming at the prediction of online consumer purchase behavior, this paper will propose a LSTM-RF prediction model based on the combination of Long Short-Term Memory (LSTM) and Random Forest (RF) by analyzing the consumer history shopping data of the e-commerce platform, so as to improve the learning ability of the prediction model and the prediction effect of the model. The experimental results show that from the overall evaluation index of the model, compared to the use of a single machine learning method to create the prediction model, the LSTM-RF prediction model combined with the LSTM algorithm and the RF algorithm has better learning ability and prediction effect.