Title: Design of e-commerce cluster information classification and extraction system based on relevant vector machine

Authors: Yanjun Sun

Addresses: School of Information Science and Engineering, Tianjin Tianshi College, Tianjin, 301700, China

Abstract: Aiming at the problems of low accuracy and long time-consuming in traditional classification and extraction system, this paper studies and designs an e-commerce clustering information classification and extraction system based on correlation vector machine. The hardware part of the system adopts modular design, and the core modules are: information preprocessing module, information representation module and classifier module. In the designed system, RVM-based classification algorithm is selected as the core algorithm of the whole system, and the algorithm is improved. The calculation method excluding the worst category in each round of comparison can effectively reduce the extraction time of information classification on the basis of protecting the classification accuracy of the original algorithm. Simulation results show that the classification extraction results of the designed system aggregate in the range of 0.4 to 0.9, and the classification extraction accuracy is high with the extraction time varying in the range of 0.1 s-0.2 s.

Keywords: relevance vector machine; e-commerce clustering; information classification and extraction; system design.

DOI: 10.1504/IJICT.2021.117536

International Journal of Information and Communication Technology, 2021 Vol.19 No.3, pp.275 - 295

Received: 17 Feb 2020
Accepted: 14 Apr 2020
Published online: 13 Sep 2021 *

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