Design and Optimization of Hotel Management Information System Based on Artificial Intelligence

Abstract

With the improvement of people's living standards, the traditional hotel management model has been unable to meet the needs of customers. The traditional hotel management model also has the defects of low efficiency. The hotel management model is also gradually developing towards the direction of intelligence. The combination of artificial intelligence technology and hotel management can not only improve the operation efficiency of the hotel but also solve the operation cost of the hotel. For customers, artificial intelligence technology can bring smarter and more comfortable accommodation conditions to customers. This study uses the convolutional neural network (CNN) and long short-term memory (LSTM) technology in artificial intelligence technology to conduct related research on the in-store mode, entertainment mode, sleep mode, and out-of-store mode in hotel management. CNN is used to extract the spatial features of hotel management, and LSTM is used to extract the temporal features of hotel management. The research results show that CNN and LSTM technology can help hotel management achieve intelligent management and optimization. CNN and LSTM techniques can better predict related factors in-store entry mode, entertainment mode, and sleep mode. For the correlated predictions of these four modes, the maximum prediction error is only 2.81%. The linear correlation coefficient also reached above 0.96. The relevant parameters of artificial intelligence technology are also suitable for the optimization and design of hotel information systems.