Title: Research on user behaviour detection algorithm of e-commerce platform based on machine learning

Authors: Yuanyuan Tang

Addresses: School of Economics and Management, Tianjin Vocational Institute, Tianjin 300410, Tianjin, China

Abstract: This paper first introduced ML, including C4.5 algorithm and support vector machine algorithm in decision tree algorithm, and introduced random forest algorithm based on ML. Then, the user behaviour of EC platform was analysed and detected. First, the problems to be solved in the EC platform behaviour analysis are determined. Then, the data was collected, and then the collected data is characterised and analysed. The extracted data was divided into training set and test set, and the algorithm model was used to analyse the data. In the experiment part, three ML algorithms, C4.5 algorithm, support vector machine algorithm and random forest algorithm, were used for data analysis. The performance of user data analysis of the three algorithms was analysed by training set and ten fold cross validation. The relative error of model classification was the lowest, which showed that ML algorithm has good data analysis ability and good application effect in the field of EC platform user behaviour detection.

Keywords: user behaviour detection; e-commerce platforms; machine learning; artificial intelligence.

DOI: 10.1504/IJCSYSE.2026.151357

International Journal of Computational Systems Engineering, 2026 Vol.10 No.1/2/3/4, pp.268 - 275

Received: 31 Oct 2023
Accepted: 04 Jan 2024
Published online: 26 Jan 2026 *

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