The environmental effect of the meetings, incentives, conventions, and exhibitions (MICE) industry is as extensive as its economic impact. Visitors attending events use a wide range of service providers, including airline car rental firms, restaurants, hotels, theaters, and tour operators. Traditionally used tourism demand forecasting approaches rely heavily on univariate time series and multivariate regression models. Although these function-based prediction systems have demonstrated some effectiveness in forecasting tourism, they are unable to accurately capture the link between tourist demand and supply as a feed-forward neural network does (FFNN). Research has shown that an FFNN can outperform regression and time-series algorithms when it comes to forecasting tourism data. This research, for the first time, expands the use of neural networks in tourist demand creation by combining a hybrid FFNN and chimp optimization learning algorithm (i.e., FFNN-ChOA) into a nonlinear tourism demand dataset. In terms of predicting accuracy, FFNN-ChOA surpasses traditional backpropagation neural networks, regression models, and time-series models.

In order to solve the problems of low efficiency of management information systems and low utilization rate of the information resources, this research paper proposes the designs of an exhibition management information system by using a B/S structure (Browser/Server mode). The proposed system is aimed at the analysis of functions and types of exhibitions. The system is comprised of the following three components: the browser-side, server-side, and middle layer. Among them, the browser includes a user management module and exhibition environment visualization module. The server includes an upper computer control module and human-computer interaction module. The middle layer includes the data resource management module, document information management module, exhibition project audit approval and management module, and information scheduling module. Experimental results show that the lowest management efficiency of the system is 96.19%, the highest can reach 98.39%, and the utilization rate of information resources is above 94%, indicating that the system has achieved the design expectation.