Dynamic Assessment and Optimization Strategy for Brand Tourism Competitiveness in the Yangtze River Delta City Cluster Based on Entropy Weight-TOPSIS

In the context of the integrated, high-quality development of the Yangtze River Delta City Cluster (YRDCC), brand tourism competitiveness is a key indicator of cities’ attractiveness and regional synergy. However, most existing studies focus on static comparisons and fail to dynamically assess competitiveness trends among cities. This study uses 27 cities in the YRDCC from 2019 to 2023 as a sample and applies the entropy weight-TOPSIS method for dynamic analysis of brand tourism competitiveness. This method integrates objective weights and relative performance across multiple indicators, enabling a comprehensive identification of city differences in resource allocation, brand communication, and service capacity. The findings reveal that Shanghai and Hangzhou lead in brand tourism competitiveness due to their strong economic foundations, rich tourism resources, and continuous brand development, playing a regional demonstration role. Suzhou and Nanjing have solid foundations but require improvements in brand internationalization and tourism experience. In contrast, Chuzhou and Chizhou lag behind due to insufficient industrial support, weak infrastructure, and low brand recognition. The study recommends enhancing brand tourism competitiveness by strengthening regional cooperation, promoting differentiated development, cultivating local brand identities, and advocating for green tourism, thereby providing a sustainable development model and empirical support for tourism development in China’s city clusters.

@article{Wang2025,
  title     = {Dynamic Assessment and Optimization Strategy for Brand Tourism Competitiveness in the Yangtze River Delta City Cluster Based on Entropy Weight-TOPSIS},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Dongmei Wang and Daoyi Wu},
  doi       = {10.14569/IJACSA.2025.0160945},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160945}
}