With the rapid development of today's social economy, tourism has also developed rapidly. According to national statistics, from 2017 to 2019, domestic tourism revenue increased from 4.57 trillion to 5.73 trillion. The tourism economy has made more and more contributions to the national economy, and it has also received more and more attention and attention from society. However, in recent years, the "explosive" growth of tourism has not only promoted economic development but also brought some challenges to society and the economy, such as environmental pollution in tourist cities. Therefore, it is of great significance to evaluate the tourism carrying capacity of a tourist destination city to realize the sustainable development of the city's tourism. This article aims to study the evaluation of urban tourism carrying capacity based on AHP and an optimized BP neural network. It designs a carrying capacity evaluation system, conducts BP neural network training for the system, and conducts system testing. The results show that the proportion of scientific and technological innovation is obviously higher than that of other aspects in the proportion of carrying capacity indicators in various aspects of each city. Environmental carrying capacity indicators can be divided into resource supply indicators, pollutant containment indicators, and social impact indicators. This article divides the important indicators into economic development, technological innovation, potential competition, environmental support, and development guarantee. Its indicators account for about 50, with an average of more than 40. This shows that the system can clearly display the main factors and evaluation indicators that affect the urban tourism carrying capacity and has certain feasibility and reliability.
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