Design of Intelligent Analysis Method for E-Commerce Data Based on Improved Bayesian Algorithm

It is frequently difficult to handle large-scale, high-dimensional data using traditional e-commerce data analysis methodologies, which makes it challenging to effectively capture user behavior and market trends. The capacity to precisely evaluate e-commerce data can be enhanced by the use of improved Bayesian algorithms, enabling more precise predictions of user behavior and market trends. This article gathers a lot of e-commerce data from e-commerce websites using web crawler technology, preprocesses it, and verifies its validity. It integrates normal distributions and dynamic modeling techniques based on Bayesian networks, and it uses a graph model to express the relationships between items like users, products, and transactions. The dataset was divided using 5-fold cross validation, and the experimental findings demonstrated that the enhanced Bayesian algorithm had an average accuracy of 97.9% for predicting user behavior. The enhanced Bayesian algorithm's average user shopping cart conversion rate is 83.2%. The analytical performance of e-commerce data can be efficiently enhanced through the use of enhanced Bayesian algorithms.