Machine Learning-Enhanced Prediction of User Behavior on e-Commerce Platforms

User behavior plays a very important role in ecommerce platforms, but there is a problem that consumer purchase behavior prediction is not ideal. Previous sales methods could not solve the problem of predicting consumer buying behavior in e-commerce platforms, and the analysis was unreasonable. Therefore, this paper proposes a machine learning enhancement algorithm for predictive analysis of consumer purchase behavior. Firstly, the simulation theory is used to process the purchase behavior data information, and the indicators are carried out according to the requirements of consumer purchase behavior processing Segmentation, reducing distractions in user behavior. Then, the simulation theory predicts and processes the purchase behavior data, forms a predictive processing scheme, and predicts the results of user behavior Conduct a comprehensive analysis. MATLAB simulation shows that under the condition that the prediction processing standard is fixed, the machine learning enhancement algorithm has the accuracy of the prediction processing of purchase behavior data User behavior judgment time is better than previous sales methods.