Forecasting agricultural tourism experience quality using internet of things and big data in China - Meng Zhu (Environmental Research Communications)
Agricultural tourism aims to facilitate the sharing of agricultural knowledge and cultural traditions through planned visits. Due to the diversity in visitor interactions and comments, measuring and forecasting the quality of tourist experiences remains a problem despite its promise. In response to this issue, this research builds a prediction model for agricultural tourism experience quality assessment using big data and the Internet of Things (ATEQA-BD-IoT). The model analyses visitor data, such as feedback, engagement levels, and knowledge gain, to forecast happiness and subsequent engagement. The approach improves the precision of experience evaluations by employing reward-based learning strategies. The outcomes show that the suggested approach successfully combines behavioural data with tourist input to generate trustworthy forecasts of tourism quality. Insightful judgments and individualized services that fulfill tourist expectations and promote sustainable tourism practices are made possible by the results, which have real-world implications for enhancing agricultural tourism experiences.