With the rapid development of the times, the financial status of many enterprises has become the top priority, and the prediction and prevention of enterprise financial risks are more important. The financial risk prediction of enterprises under big data can better collect data and analyze it, which can help workers bring convenience. In order to make the public better understand the corporate financial risk forecasting, the research on the prediction and prevention of corporate financial risk based on big data analysis is as follows: (1) the broad and narrow senses of corporate finance readers gain a clearer understanding of the importance of corporate finance; (2) an introduction to the calculation algorithm of enterprise financial risk, which facilitates the staff to better calculate the financial risk of the enterprise and establish a financial risk model; (3) conduct an example investigation on a representative pharmaceutical company, analyze its various financial indicators, and compare with the indicators in the same industry to judge whether the financial data is normal; and (4) conduct comparative research on corporate finance under big data and find that big data can better prevent corporate financial risks. It is concluded that this risk prediction method is very effective. It shows that corporate financial risk is very important to social development, and based on big data, risks can be better predicted and prevented.
We provide a brief overview of the connotation and characteristics of data mining technology in the era of big data, analyze the feasibility of data mining technology in business management from the economic and technical perspectives, and propose specific application suggestions according to the content and requirements of business management. This paper describes in detail the principles and steps of using the weighted plain Bayesian algorithm and the decision tree algorithm to analyze students’ performance; firstly, we need to obtain the plain Bayesian analysis model of college students’ learning literacy in physical education and the C4.5 graduation literacy analysis model, and then use certain rules to combine the weighted plain Bayesian algorithm and the decision tree algorithm to obtain the WNB-C4.5 college students’ learning literacy analysis model. In addition, in the prediction of financial risks, the classification scheme can be used in the judgment of violation of regulations, but the most used classification scheme is the decision tree. Experiments show that the effectiveness of this scheme in data mining for financial companies is increased by 2% compared to the benchmark method.
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