Title: Human resource management and organisation decision optimisation based on data mining

Authors: Mianmin Zeng

Addresses: School of Business, Macau University of Science and Technology, Macau, 999078, China

Abstract: The utilisation of big data presents significant opportunities for businesses to create value and gain a competitive edge. This capability enables firms to anticipate and uncover information quickly and intelligently. The author introduces a human resource scheduling optimisation strategy using a parallel network fusion structure model. The author's approach involves designing a set of network structures based on parallel networks and streaming media, enabling the macro implementation of the enterprise parallel network fusion structure. Furthermore, the author proposes a human resource scheduling optimisation method based on a parallel deep learning network fusion structure. It combines convolutional neural networks and transformer networks to fuse streaming media features, thereby achieving comprehensive identification of the effectiveness of the current human resource scheduling in enterprises. The result shows that the macro and deep learning methods achieve a recognition rate of 87.53%, making it feasible to assess the current state of human resource scheduling in enterprises.

Keywords: big data analysis; human resource; enterprise management; parallel network; scheduling optimisation.

DOI: 10.1504/IJDMB.2024.139476

International Journal of Data Mining and Bioinformatics, 2024 Vol.28 No.3/4, pp.439 - 452

Received: 23 May 2023
Accepted: 26 Oct 2023
Published online: 02 Jul 2024 *

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