Performance Evaluation of Integrated Learning Algorithms for E-Commerce Product Classification and Prediction

Abstract

 In the booming e-commerce industry, product classification and sales prediction play a key role in the efficient operation of e-commerce platforms, and have been receiving increasing attention. As an important technology in the field of machine learning, integrated learning algorithms have been widely used in e-commerce scenarios. The algorithm is able to synthesise multi-dimensional attributes of products, such as brand, price, function, user evaluation, etc., to accurately classify various types of products and significantly improve the classification accuracy. At the same time, the integrated learning algorithm can also handle complex e-commerce data containing text, images, numerical values, and other types, and has strong robustness, which reduces the impact of data noise, missing values, and other problems on the classification results. The fusion of integrated learning and deep learning, adaptation to the big data environment, and enhancement of the interpretability of the algorithms will be important directions for its development. This research not only provides e-commerce practitioners and researchers with a reference basis for algorithm selection, but is also expected to promote e-commerce platforms to optimise their operation strategies and enhance market competitiveness.