In order to improve the ability of enterprises to deal with financial risks, reduce labor costs, reduce financial losses, increase investors' trust in enterprise finance, and establish a comprehensive enterprise financial risk evaluation index system, the deep learning technology and data mining method under the artificial intelligence environment are applied to the financial risk analysis of listed companies. Under this background, an analysis method of financial risk prevention based on interactive mining is put forward. Around the various financial risks faced by listed companies, a special risk analysis model is established to analyze the key factors. Through the empirical analysis of 21 listed companies, rules with high trust are found, and the financial crisis of listed companies is forewarned in time. The results show that the financial risk evaluation index system of four dimensions of solvency, operation ability, profitability, growth ability and cash flow ability can affect the financial risk of enterprises. Compared with the traditional data mining algorithm, the algorithm of financial risk index evaluation model constructed in this exploration has the best performance, and the average detection accuracy is 90.27%. The accuracy of the model can be improved by 30%. The results show that the weight of each variable is good, and all of them pass the consistency test. The evaluation effect is high, and the relative error is 1.55%, which proves the rationality and accuracy of the model. The financial risk prevention model based on deep learning and data mining technology can provide a theoretical basis for the research of enterprise financial risk prevention.

为了提高企业应对财务风险的能力，降低人工成本，减少财务损失，提高投资者对企业财务的信任度，建立完善的企业财务风险评估指标体系，深度学习技术和数据挖掘方法，将人工智能环境应用于上市公司财务风险分析。在此背景下，提出了一种基于交互式挖掘的金融风险防范分析方法。针对上市公司面临的各种财务风险，建立了特殊的风险分析模型来分析关键因素。通过对21家上市公司的实证分析，发现信任度高的规则，并及时预警了上市公司的财务危机。结果表明，偿付能力，经营能力，盈利能力，增长能力和现金流量能力四个维度的财务风险评价指标体系可以影响企业的财务风险。与传统的数据挖掘算法相比，本次探索构建的金融风险指数评价模型算法性能最好，平均检测准确率为90.27％。该模型的准确性可以提高30％。结果表明，每个变量的权重都很好，并且都通过了一致性测试。评估效果高，相对误差为1.55％，证明了该模型的合理性和准确性。