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.

中文翻译：

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基于长短期记忆（LSTM）的旅游安全预警信息系统优化算法

旅游安全是旅游业关注的焦点。它不仅关系到游客的生命财产安全，也关系到社会稳定和旅游业的可持续发展。但不少景区的安全预警侧重于安全事件发生后的应对措施和补救方案，不少景区工作人员安全意识和信息分析能力有限，容易出现信息发布滞后的情况，并且不关注潜在安全问题的信息。因此，本文研究基于LSTM模型的旅游安全预警信息系统的优化算法，利用循环神经网络和LSTM提高时序数据的处理和预测能力。实验结果表明，基于LSTM模型的旅游安全预警信息系统中3个隐藏层的数量可以减少模型的训练时间，提高性能。与基于BP神经网络的旅游安全预警信息系统相比，它具有更好的准确性和稳定性，对时间序列数据有更好的处理和预测能力，可以实时科学地监测和分析数据，动态分析数据。