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At present, under the huge competitive pressure of the market, although listed companies have more development opportunities, they are facing more severe challenges and risks at the same time. At present, there are many cases of listed companies falling into trouble due to financial crisis (Sun et al., 2019). On the other hand, with the rapid development of information technology, database, network, data mining and other technologies are increasingly mature, which makes it possible to build an efficient financial early warning system. Financial crisis early warning is based on the actual or predicted financial indicators, according to some early warning method to judge the current or future financial situation of Listed Companies (Phengsuwan et al., 2019). In recent years, with the continuous development of big data analysis, support vector machine based financial security risk detection method, neural network based financial security risk detection method, they have a certain self-organization learning ability, can effectively fit the characteristics of financial security risk changes, financial security risk detection results are better, become an important research direction of financial security risk detection. The establishment of listed companies' financial risk intelligent early warning system enables the management to find the causes of the financial crisis and the hidden problems in the company's financial management system through a comprehensive analysis of the company's internal operation and external environment, and formulate effective measures to solve the problems, so as to avoid or reduce the company's financial crisis (Kurum et al., 2018). At the same time, the establishment of financial early warning system of listed companies is also conducive to the decision-making of investors and the regulation of the securities market. Therefore, the scientific and reasonable financial risk intelligent early warning of listed companies is of great significance to reduce the financial crisis of listed companies and develop continuously, stably and healthily.
At present, there are two types of intelligent early warning research on financial risk of listed companies: one is based on statistics (Ji Kuixiu et al., 2018), the other is based on artificial neural network (Liu Wenjun et al., 2020). Statistical early-warning methods need to rely on historical samples, and must meet the assumption of multiple normality. The learning of early-warning knowledge is indirect, inefficient and lack of dynamic early-warning ability. The artificial neural network early warning method can overcome some shortcomings of statistical methods, has self-learning ability, and can carry out dynamic early warning. Because of their high predictive capabilities and structured approach, Artificial Neural Networks are effective data-driven modelling tools for nonlinear systems dynamic modelling and identification. It is effective in automating the process of learning rules from diverse applications because of their capacity to learn and generalize from data, which mimics the human ability to learn from experience. Artificial Neural Networks are also notable for their capacity to cope successfully with multifaceted issues involving hundreds of attributes (Anbarasan et al., 2020). However, many of the factors leading to the crisis of listed companies may be financial or non-financial, such as market, politics, technology and so on. It does not include the financial early warning management module, its financial analysis does not consider the retrieval of the most matching cases, the accuracy of financial risk early warning is low, and the early warning time is long, which cannot meet the functional requirements of modern listed company management. Intelligent early warning systems might aid in the prevention of financial crises by following a structured prediction of unfavorable occurrences. Early warning systems are primarily used to anticipate crises before they do harm and to reduce false alarms of impending crises (Gao et al., 2020). Therefore, the financial risk intelligent early warning system of listed companies based on genetic tabu algorithm is constructed.