The purpose is to make full use of data mining and machine learning technology under big data to improve the ability of trade financial enterprises to cope with the risk of excessive financialization. In view of the above needs, based on previous studies, genetic algorithm (GA), neural network and principal component analysis (PCA) methods are used to collect and process the data, and build a risk assessment model of excessive financialization of financial enterprises. The performance of the model is analyzed through the data of specific cases. The results suggest that the data mining technology based on back propagation neural network (BPNN) can optimize the input variables and effectively extract the hidden information from the data. The specific examples show that most of the current enterprises do not have greater financial risk. However, most of the financial enterprise indexes show that the actual enterprise assets are gradually financialized. The total accuracy rate of financial risk assessment model based on deep belief network (DBN) is over 91%, and the accuracy of the model can reach 80% even if the sample size is small. Therefore, the financial risk assessment model proposed can effectively analyze the relevant financial data, and provide reference for the financial decision-making research of financial enterprises.

目的是充分利用大数据下的数据挖掘和机器学习技术，提高贸易金融企业应对过度金融化风险的能力。针对上述需求，在前人研究的基础上，采用遗传算法（GA）、神经网络和主成分分析（PCA）等方法对数据进行采集和处理，构建金融企业过度金融化风险评估模型。通过具体案例的数据分析模型的性能。结果表明，基于反向传播神经网络（BPNN）的数据挖掘技术可以优化输入变量，有效地从数据中提取隐藏信息。具体事例表明，目前大多数企业没有更大的财务风险。然而，大多数金融企业指标表明，企业实际资产正在逐步金融化。基于深度信念网络（DBN）的金融风险评估模型总准确率超过91%，即使样本量很小，模型的准确率也可以达到80%。因此，所提出的财务风险评估模型可以有效地分析相关财务数据，为金融企业的财务决策研究提供参考。