Tourism Demand Forecasting based on Adaptive Neural Network Technology in Business Intelligence

In order to improve the effect of demand forecasting, this paper combines the adaptive neural network technology to construct a demand forecasting model, and proposes an adaptive fault-tolerant PI control strategy for multiple-input and multiple-output systems. In order to verify the effectiveness and stability of the designed adaptive fault-tolerant PI control strategy, this paper uses an iterative algorithm to improve the tracking control accuracy. Moreover, this paper integrates temporal geography with the theory of probabilistic temporal geography, system analysis theory, and tourist flow space-time bayonet theory to propose a research framework for forecasting tourist flow spatio-temporal bayonet based on the WITNESS simulation system. The research shows that the tourism demand forecasting model based on adaptive neural network technology proposed in this paper has good tourism demand forecasting effect, and has a certain effect on promoting the development of tourism.