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

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  • Wang, Zeyu
  • Deng, Yue
  • Zhou, Shouan
  • Wu, Zhongbang

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.

Suggested Citation

  • Wang, Zeyu & Deng, Yue & Zhou, Shouan & Wu, Zhongbang, 2023. "Achieving sustainable development goal 9: A study of enterprise resource optimization based on artificial intelligence algorithms," Resources Policy, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:jrpoli:v:80:y:2023:i:c:s0301420722006559
    DOI: 10.1016/j.resourpol.2022.103212
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    Cited by:

    1. Wang, Huaxing & Li, Tianzi & Zhu, Junfan & Jian, Youting & Wang, Zeyu & Wang, Zengwen, 2023. "China's new environmental protection law: Implications for mineral resource policy, environmental precaution and green finance," Resources Policy, Elsevier, vol. 85(PB).
    2. Yuwei Liu & Shan Qin & Jiamin Li & Ting Jin, 2023. "Artificial Intelligence and Street Space Optimization in Green Cities: New Evidence from China," Sustainability, MDPI, vol. 15(23), pages 1-15, November.
    3. Xiaoya Hu & Huimin Huang & Jun Ruan & Weijia Wang, 2023. "Pollution Reduction, Informatization and Sustainable Urban Development—Evidence from the Smart City Projects in China," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    4. Run Tang & Xin Guan & Junfan Zhu & Bo Liu & Zeyu Wang & Fanbao Xie, 2023. "Evaluation of Sustainable City and Old-Age Security Policy Intervention in China," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    5. Gao, Shiya & Guan, Xin & Tang, Run & Zhu, Junfan & Wang, Zeyu & Xu, Wei, 2023. "Resource curse, economic efficiency and green recovery based on three-subject framework," Resources Policy, Elsevier, vol. 85(PB).
    6. Yang, Ya & Zhou, Mengru & Hou, Yawei & Tang, Run & Liu, Bo & Deng, Yue, 2023. "Examining the impacts of implicit economic policy on urban environmental pollution: Unveiling pathways for sustainable recovery," Resources Policy, Elsevier, vol. 85(PA).
    7. Shuhui Yu & Xin Guan & Junfan Zhu & Zeyu Wang & Youting Jian & Weijia Wang & Ya Yang, 2023. "Artificial Intelligence and Urban Green Space Facilities Optimization Using the LSTM Model: Evidence from China," Sustainability, MDPI, vol. 15(11), pages 1-14, June.

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