Human resource management risks are due to the failure of employer organization to use relevant human resources reasonably and can result in tangible or intangible waste of human resources and even risks; therefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction. The back propagation (BP) neural network is an information analysis and processing system formed by using the error back propagation algorithm to simulate the neural function and structure of the human brain, which can handle complex and changeable things that do not have an obvious linear relationship between output results and input factors, so as to find the objective connection between the two. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. The study results show that the early warning model of human resource management risk based on the BP network is effective, and this trained and tested BP network risk warning model can be used to conduct early warning empirical research on human resource risks to prevent human resource risks, ensure enterprise’s benign operation, and at the same time play a role in supervision and promotion of market order rectification.

中文翻译：

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基于BP神经网络的人力资源管理风险预警模型的分析与仿真。

人力资源管理风险是由于用人单位未能合理使用相关人力资源，可能导致有形或无形的人力资源浪费，甚至带来风险；因此，构建实用的人力资源管理风险预警模型对风险的早期预测至关重要。反向传播（BP）神经网络是通过使用误差反向传播算法来模拟人脑的神经功能和结构而形成的信息分析和处理系统，该系统可以处理没有明显线性关系的复杂和多变的事物在输出结果和输入因子之间建立联系，从而找到两者之间的客观联系。在总结和分析以前的研究成果的基础上，阐述了人力资源管理风险预警的研究现状和意义，阐述了BP神经网络的发展背景，现状和未来挑战，介绍了BP神经网络的联系权重计算和学习训练的方法和原理。进行了人力资源管理的风险诱因分析，指标体系建立和网络节点选择，构建了基于BP神经网络的人力资源管理风险预警模型，基于BP神经网络进行了风险预警模型的训练与检测网络，最后进行了仿真及其结果分析。研究结果表明，基于BP网络的人力资源管理风险预警模型是有效的，