Achieving sustainable development goal 9: A study of enterprise resource optimization based on artificial intelligence algorithms

Zeyu Wang, Yue Deng, Shouan Zhou and Zhongbang Wu

Resources Policy, 2023, vol. 80, issue C

Abstract: Under the rapid economic development trend, exploring the resource optimization strategy of cultural and creative enterprises for sustainable socio-economic development is highly relevant. This study applies the recommendation system to decision-making and resource optimization of entrepreneurial projects for the current situation of complexs entrepreneurial enterprises in the cultural and creative industry (CCI). The neural network algorithm (NNA) is adopted to model project features, user's behavioral and content features. Finally, a recommendation and resource optimization model based on NNA is constructed for CCI-related entrepreneurial projects, and the model is evaluated and analyzed. The results demonstrate that with the increase in the training period, the model's recognition accuracy reaches 81.64%. Besides, the prediction error of the recommender system is minimized when the word vector length is 200, and the number of implied features is 200. Therefore, the entrepreneurial project recommendation and resource optimization model can significantly improve the recognition accuracy and reduce prediction errors, providing experimental references and contributing to the subsequent sustainable development of social economy and entrepreneurial resource optimization.

Keywords: Deep learning; AI Algorithms; Entrepreneurship resources; Sustainable development (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0301420722006559
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:80:y:2023:i:c:s0301420722006559

DOI: 10.1016/j.resourpol.2022.103212

Access Statistics for this article

Resources Policy is currently edited by R. G. Eggert

More articles in Resources Policy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().