<p>Exploring the rationality of hotel location selection is of significant importance for optimizing urban spatial structure and improving tourism service levels. Artificial intelligence provides a data-driven approach for hotel location selection. Therefore, this paper takes the star-rated hotels in the six districts of Tianjin as the research subject and proposes a few-shot hotel location prediction method based on meta-learning algorithms and transportation accessibility. First, the initial location prediction results are obtained through the meta-model. Then, a transportation accessibility calculation model is constructed using spatial syntax for secondary screening. Finally, an appropriateness distribution map is created according to demand levels. The results show that: (1) The meta-model achieves a classification accuracy of 90.45% for star-rated hotels, with a location fitting degree of 91.90%, an improvement of approximately 11% compared to the baseline model; (2) Transportation conditions play a crucial role in the distribution of star-rated hotels, contributing 45% of the classification information; (3) It is recommended that future investments in star-rated hotels focus on areas around Xiaobailou Street, Dawangzhuang Street, and Wudadao Street. Additionally, to verify the overall effectiveness of the model, a comparison with other datasets demonstrates the performance advantages of the meta-model in few-shot location scenarios, providing a practical research path for hotel location decision-making.</p>