Abstract
Tourism safety is the focus of the tourism industry. It is not only related to the safety of tourists' lives and property, but also related to social stability and sustainable development of the tourism industry. However, the security early warning of many scenic spots focuses on the response measures and remedial plans after the occurrence of security incidents, and the staff of many scenic spots have limited security awareness and information analysis ability, which is prone to lag in information release, and do not pay attention to the information of potential security problems. Therefore, this paper studies the optimization algorithm of the tourism security early warning information system based on the LSTM model and uses the recurrent neural network and LSTM to improve the processing and prediction ability of time-series data. The experimental results show that the number of three hidden layers in the tourism security early warning information system based on the LSTM model can reduce the training time of the model and improve the performance. Compared with the tourism safety early warning information system based on the BP neural network, it has better accuracy and stability, has better processing and prediction ability for time series data, and can monitor and analyze data scientifically in real-time and dynamically analyze data.
Highlights
Tourism has gradually become one of the important industries
There are many unfortunate events that tourists encounter in the process of tourism, such as abrupt weather changes in marathon competitions in natural scenic spots, stampede on the Bund of Shanghai caused by too many tourists, tsunami, kidnapping of tourists, and constant theft in tourist areas, which have caused serious adverse effects on the tourism industry and restricted the sustainable development of the tourism industry [4, 5]. erefore, tourism security has become a highly valued and concerned issue in various countries and regions, and tourism security early warning has become an inevitable trend of tourism development
Construction and Optimization of Tourism Security Early Warning Information System Based on LSTM. e tourism safety early warning information system is used to predict and warn the changes of scenic spots in the future from multiple dimensions according to the reasonable index system and scientific methods. erefore, the influencing factors of tourism safety early warning information are diversified and nonlinear
Summary
Tourism has gradually become one of the important industries. As people no longer meet the basic needs of life, more and more people begin to pursue high-quality life. Is paper studies the optimization algorithm of the tourism security early warning information system based on the LSTM model. Compared with the traditional tourism security early warning methods, the artificial neural network has better fault tolerance and stronger robustness It can quickly process data and find the corresponding optimal solution, and its nonlinear thinking can well deal with the relationship between many factors. Compared with the BP neural network, the LSTM model can better process temporal information and realize the purpose of real-time processing tourism safety early warning information. The LSTM model is used to construct the tourism safety early warning information system, which improves the processing ability of the system to temporal information, so as to realize the purpose of real-time dynamic information supervision and analysis.
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