Title: The feature classification method of mobile e-commerce big data under the webcast mode

Authors: Jie Li

Addresses: School of Economics and Management, Fujian Vocational and Technical College of Water Conservancy and Electric Power, Yongan, 366000, China

Abstract: In order to improve the special effect of classification results and reduce the convergence classification tolerance, a feature classification method of mobile e-commerce big data under the webcast mode was designed in this paper. Firstly, the dynamic mining of mobile e-commerce big data is realised by setting sliding windows and according to the association mining rules between data. Then, the data weight is calculated based on the TF-IDF method, and the data features are extracted through normalisation processing. Finally, after the data features are derived, the multi-label merging is implemented, and then the feature classification is realised by traversing the feature values using the fuzzy mathematics theory. Experimental results show that the classification specificity interval of this method is [93.1%, 95.6%], and the convergence classification tolerance interval is [0.029, 0.055], indicating that the reliability of this method is high.

Keywords: webcast mode; mobile e-commerce data; dynamic mining; fuzzy mathematics; feature classification.

DOI: 10.1504/IJNVO.2023.135951

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.244 - 256

Received: 24 Feb 2023
Accepted: 12 Jun 2023
Published online: 10 Jan 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article