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Evaluation model of South China Sea tourism venture capital based on improved GA neural network under the background of health tourism industry development

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  • Minjie Lin

    (Hainan College of Economics and Business)

Abstract

To assess investment risks in the health industry in the South China Sea, project analysis and expert experience were used to obtain risk assessment data. To account for the numerous risks posed by investing in tourism in the South China Sea, we employed a multi-level grey analysis method to create an evaluation index system for tourism risk investment in the region. At the same time, the GA-BP South China Sea risk investment evaluation model is adopted, and the impact relationship of various indicators is quantified. The simulation experiment results demonstrate that, in the function loss test outcomes of risk sample 1, BP’s function loss was 1.02, PSO’s function loss was 0.79, and GA-BP’s function loss was 0.125, following 40 training iterations. GA-BP has better function loss performance. During the training function output test of each model, the GA-BP model exhibits the best output performance and can precisely generate output results on South China Sea venture capital. The research content has important reference value for the development of marine resources and investment in the corresponding tourism industry.

Suggested Citation

  • Minjie Lin, 2025. "Evaluation model of South China Sea tourism venture capital based on improved GA neural network under the background of health tourism industry development," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(2), pages 4185-4201, February.
    • Handle: RePEc:spr:endesu:v:27:y:2025:i:2:d:10.1007_s10668-023-04069-0
    DOI: 10.1007/s10668-023-04069-0
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    References listed on IDEAS

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    1. Luo, Jian & Yan, Xin & Tian, Ye, 2020. "Unsupervised quadratic surface support vector machine with application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 280(3), pages 1008-1017.
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