Title: An intelligent mining method for network marketing potential user based on random forest algorithm

Authors: Xiuming Yu

Addresses: School of Electronic Engineering, Changchun College of Electronic Technology, Changchun, 130012, China

Abstract: In this paper, an intelligent mining method for network marketing potential users based on random forest algorithm is proposed. Utilise web crawling technology to collect user behaviour data for network marketing. Cluster the collected data using the Literal Fuzzy C-Means (LFCM) algorithm to obtain clustered user behaviour data. Input the data from the user behaviour dataset into the Latent Dirichlet Allocation (LDA) model to obtain the theme extraction results of network marketing user behaviour documents. Combine the extracted topics with the random forest algorithm to achieve intelligent mining of potential users in network marketing. The experimental results show that the proposed method achieves a maximum accuracy of 98.45% and a maximum recall of 99.14%, with a processing time varying between 0.23 s and 0.71 s. This approach can be widely applied to potential user mining in network marketing.

Keywords: random forest algorithm; network marketing; potential user; intelligent mining method; LFCM algorithm; LDA model.

DOI: 10.1504/IJCAT.2026.154050

International Journal of Computer Applications in Technology, 2026 Vol.78 No.4, pp.323 - 335

Received: 14 Feb 2025
Accepted: 18 Jun 2025
Published online: 10 Jun 2026 *

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