Title: The personalised classification of brand promotion information based on K-means algorithm

Authors: Xi Li

Addresses: Department of Art and Design, Guangxi University of Foreign Languages, Nanning, 530222, Guangxi, China

Abstract: In order to improve the efficiency of personalised classification of brand promotion information and shorten the time of personalised classification, this paper proposes a personalised classification method of brand promotion information based on K-means algorithm. First, collect brand promotion information, and calculate the text relevance of brand promotion information through weighting factors. Secondly, the attribute division of extension information is carried out by using the three branch decision-making theory. Then, the information features of brand promotion are extracted by capsule network. Finally, calculate the similarity between different brand promotion information, obtain the brand promotion information classification function, and realise the personalised classification of brand promotion information through k-means algorithm. The experimental results show that the classification accuracy of this method is 98.08%, and the time of personalised information classification is only 1.20 s, indicating that this method can effectively improve the efficiency of personalised classification of brand promotion information.

Keywords: k-means algorithm; information attribute division; feature extraction; personalised classification of information.

DOI: 10.1504/IJITM.2025.144114

International Journal of Information Technology and Management, 2025 Vol.24 No.1/2, pp.117 - 129

Received: 28 Jun 2022
Accepted: 23 Sep 2022
Published online: 28 Jan 2025 *

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