Quantitative Evaluation of Digital Economy Policies in the Tourism Industry Using the TF-IDF and PMC Index Model

With the acceleration of globalization, the development of the tourism industry increasingly relies on cross-border collaboration, and the digital economy can provide new opportunities for the tourism industry and promote the global application of new tourism productivity. The current research focuses on how to formulate policies and measure their implementation effects, providing important basis for optimizing strategies. Therefore, the joint term frequency inverse document frequency algorithm and potential Dirichlet allocation model are used to analyze policy texts, and the policy consistency index model is introduced to quantitatively evaluate the digital economy policies of the tourism industry. The research results showed that the keywords of tourism industry and platform, promotion service, technology and application appeared together more than 1000 times in policy texts. The depression indices corresponding to policy documents 1 to 8 were 2.944, 2.049, 2.252, 1.269, 2.045, 1.667, 2.142, and 4.118, respectively, with an average depression index of 2.311. In the proportion of depression index results, the policy effect had the highest proportion of 0.5, indicating that it was the most important, while policy guarantee and policy field importance were second, both corresponding to 0.4. In the final analysis, the robustness results at the 95% significance level showed that the advantages and disadvantages of digital economy policies in the tourism industry still had significant positive effects on the digital transformation of regional enterprises. Therefore, this research method can accurately analyze the implementation effect and internal mechanism of policies, fully utilize big data to provide scientific basis for government decision-makers and business operators, and adjust policy measures in a timely manner.