Research on Carrying Capacity and Resource Management of Tourism Destinations Combined with Machine Learning

Tourism is an important engine of economic development all over the world. However, the sustainability and resource management of tourist destinations have always been a concern. The purpose of this study is to explore how to combine machine learning(ML) technology to improve the carrying capacity and resource management of tourist destinations in order to improve their competitiveness and sustainability. In this paper, ML method is introduced, and based on the evaluation theory of tourism destination carrying capacity, an evaluation method of tourism destination carrying capacity based on ML algorithm is proposed to evaluate the carrying capacity of regional tourism destinations. By analyzing a large number of relevant data and applying ML algorithm, we can more accurately estimate the number of tourists, traffic distribution, peak hours and other information, thus helping destination managers to plan and allocate resources more effectively. In addition, ML can be used for resource management, improving resource utilization efficiency, reducing waste and reducing environmental impact. Finally, ML can also contribute to risk management, so that destinations can better cope with emergencies and protect the safety of tourists and residents.