Title: E-commerce customer marketing classification technology based on improved ant colony clustering algorithm

Authors: Ming Zhong

Addresses: Business School, Guangxi Technological College of Machinery and Electricity, Nanning, 530007, China

Abstract: The vigorous development of the internet has driven the development of the e-commerce market economy. Facing a huge number of consumers, every e-commerce enterprise is facing the problem of customer classification. To address this issue, the collected customer characteristic data are processed by feature selection and principal component analysis for dimensionality reduction. According to the ant colony clustering model, a new two-dimensional data object load state matrix is introduced, and by improving the ant's. Observe the radius and introduce the Sigmoid function to improve the test accuracy. Test findings demonstrate that the F-measure value of the standard model is 0.846, and the F-measure value of the improved model is 0.934. The former has an error of 0.25 after 500 iterations, and the error of the later is 0.12 after 300 iterations. The average consumption time of the standard model test dataset is 51.64 s, and the average consumption time of the improved model is 28.12 s. The test's findings reveal that the improved method has smaller error value and shorter time consumption when dealing with discrete data, and its performance is better than the standard model, which can better classify customers. The growth of e-commerce has been greatly influenced by the research findings.

Keywords: e-commerce; customer classification; marketing; data processing; ant colony clustering; ACC.

DOI: 10.1504/IJCSYSE.2026.151356

International Journal of Computational Systems Engineering, 2026 Vol.10 No.1/2/3/4, pp.295 - 305

Received: 08 Nov 2023
Accepted: 16 Jan 2024
Published online: 26 Jan 2026 *

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