The proliferation of corporate financial data makes it more and more difficult to mine from a large number of accounting statements that have value and identify potential risks of a company. This study collects the historical accounting statement data of Company J from 2020 to 2024, and screens out the key financial indicators through data mining techniques. Meanwhile, the financial risk status of Company J in recent years is analyzed by combining the accounting statement data. Then, using the probability of the company’s financial risk as the dependent variable, the correlation between the financial indicators and the probability of risk emergence is analyzed, and a regression prediction model is established. The total assets of Company J in 2020~2024 increase year by year, and by the end of 2024, the total assets reach 367.41 billion yuan. The company’s short-term solvency in 2020~2023 is weak, and the cash ratio, quick ratio, and current ratio are lower than the industry average. The study extracted six main factors affecting the company’s financial risk, and its overall variance is 85.58%. Logistic regression analysis shows that a 1-unit increase in the values of the six main factors reduces the likelihood of the company’s financial risk by 0.209~4.056. This study provides a quantifiable analytical method for accounting statement data mining, which can help enterprises to strengthen the control of financial risk and provide references for investment analyses. Provide reference.
