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The implementation of leisure tourism enterprise management system based on deep learning

机译:基于深度学习的休闲旅游企业管理系统的实施

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摘要

The foremost part of the leisure tourism enterprise management system is evaluated and studied to explore the financial risk of leisure tourism enterprise and find the loopholes in enterprise risk management. First, the current financial risk management of tourism enterprises is evaluated, using the solvency of corporate finance, capital structure, operating efficiency, and profitability as indexes. Then, the backpropagation neural network (BPNN) model is constructed through the neural network in deep learning. Consequently, the BPNN algorithm model is used to identify and address risks and analyze the financial risks in the risk management system of leisure tourism enterprises. The results show that the shareholders' equity ratio has a great influence on the financial security of tourism enterprises; most of the tourism enterprises have a good financial situation, and most of them do not have large financial risk, and most of them can counter the debt risk properly. Thus, the BPNN model can effectively improve the efficiency and quality of the risk management system in traditional tourism enterprises. The results can help tourism enterprises utilize the enterprise management system better.
机译:评估和研究休闲旅游企业管理体系的最远部分,探讨了休闲旅游企业的金融风险,并找到了企业风险管理中的漏洞。首先,利用企业金融,资本结构,营业效率和盈利能力的偿付能力来评估旅游企业的现行金融风险管理。然后,通过深度学习中的神经网络构建了反向化神经网络(BPNN)模型。因此,BPNN算法模型用于识别和解决风险,并分析休闲旅游企业风险管理体系中的金融风险。结果表明,股东股权比对旅游企业的金融安全有很大影响;大多数旅游企业都有良好的财务状况,大多数都没有大量的财务风险,大多数人都可以正确抵制债务风险。因此,BPNN模型可以有效地提高传统旅游企业风险管理系统的效率和质量。结果可以帮助旅游企业利用企业管理体系更好。

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