An Early Warning Mechanism for the Environmental Carrying Capacity of Forest Tourist Attractions and Hotels Online Operations Based on Big Data Integration Technology

This article attempts to introduce the BP neural network model to construct an early warning system for the environmental carrying capacity of forest tourist attractions and hotels online operations, and to verify and analyze the case of scene area in the National Forest Park. The results show that the BP neural network model is feasible to construct an early warning system for the environmental carrying capacity of forest tourist attractions, and it can realize the dynamic early warning evaluation of the current and future tourist environmental carrying conditions of the scenic spot. In order to reduce the degree of influence of the results of the Zmeans algorithm, based on the previous research and discussion on the clever subs warm algorithm the range that the environment can be automatically restored, and to calculate the environmental the particle swarm optimization algorithm was optimized, and the T double particle s warm optimization algorithm was proposed environment carrying capacity of the scenic spot.