Title: Research on talent development for empowering sustainable development of night economy

Authors: Wuyan Lou

Addresses: School of Art, Zhejiang Shuren University, Hangzhou, 310015, China

Abstract: The night economy has been expanding in scale in recent years, while the effective screening and training of talents is an important factor for sustainable economic development. At present, the problem of talent recruitment and training of night economy is that it is difficult to exchange information between talents and recruiters. In this study, a talent classification algorithm based on Improved RBF neural network and self-attention mechanism is constructed. The performance test results of the algorithm show that the accuracy of the algorithm reaches up to 93%, the specificity also reaches up to 90%, and the success rate of talent recruitment reaches 21%. These results show that the talent classification algorithm based on the improved RBF has the value of practical application to talent discovery and cultivation in the night economy.

Keywords: night economy; sustainable development; RBF neural network; self-attentive mechanism; talent development.

DOI: 10.1504/IJKBD.2023.133327

International Journal of Knowledge-Based Development, 2023 Vol.13 No.2/3/4, pp.248 - 262

Received: 18 Aug 2022
Accepted: 31 Oct 2022
Published online: 12 Sep 2023 *

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