Title: Rural e-commerce data analysis based on data mining and its enlightenment to rural digital economy management

Authors: Jingyang Tang

Addresses: Furong College, Hunan University of Science and Arts, Changde, 415000, Hunan, China

Abstract: In the context of the era of big data, the comprehensive enhancement and managerial refinement of rural e-commerce can be achieved through the profound and real-time scrutiny of rural e-commerce data. However, only relying on a single fusion algorithm is not enough to meet the needs of data monitoring system. This research manuscript concentrates on the realms of multi-source data acquisition, transmission, display, and fusion algorithms, resulting in a fully automated rural e-commerce data monitoring system crafted through the skilful use of data mining and various other computational operations. In addition, this paper selects six keywords that reflect consumers' attention appropriately, and uses dimensionality reduction techniques on historical transaction data. Finally, simulation experiments show that the combined model has higher prediction accuracy than the single prediction model, and the average prediction accuracy of rural e-commerce data in the next seven days reaches about 97%.

Keywords: multi-source data; rural e-commerce; MDS dimension reduction model; long short-term memory; LSTM prediction model; digital transformation; data mining.

DOI: 10.1504/IJDMB.2024.137751

International Journal of Data Mining and Bioinformatics, 2024 Vol.28 No.2, pp.168 - 180

Received: 12 May 2023
Accepted: 07 Sep 2023
Published online: 04 Apr 2024 *

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