Multi-Dimensional Analysis of CST Industry Development via Text Mining

With the accelerating shift of tourists' demand from traditional sightseeing to in-depth experience, although Shenyang is rich in culture, sports and tourism (CST) resources, it is faced with structural contradictions such as unitary resource development, insufficient innovation and transformation, deepening of integration, imperfect brand system, etc., and there is still a gap in the construction of indicator system in the existing research. This study employs a cross-modal algorithmic framework integrating the Hidden Markov Model and TF-IDF algorithm to mine online textual data, thereby constructing a multidimensional indicator system for Shenyang’s CST industry development. Additionally, questionnaire data are analyzed through Pearson correlation analysis to identify variable interdependencies, CRITIC weight analysis to objectively prioritize key factors, and configuration analysis to unravel synergistic mechanisms among determinants. The results show that through the innovative application of the above cross-modal algorithm fusion, the quantitative analysis of the development of Shenyang’s CST industry can be effectively cracked, and the multi-dimensional identification and accurate capture of user needs can be realized.