The study on the legal regulation of Internet financial crime holds significant importance in the context of the rapid development of China’s Internet finance and the increasing prevalence of crime in this sector. To prevent crime, attention towards effective control of Internet finance crime has grown, emphasizing the protection of consumers’ rights, reduction of economic damage, and promotion of Internet finance development. Data processing for criminal acts on Internet finance platforms is crucial, with the utilization of random forest algorithms, including Decision tree and Bagging integration algorithms. The methodology employed in this study is quantitative, focusing on the analysis of Internet financial crime using random forest and association rules based on data processing. The experiment results about the random forest in this paper showed that when the test sample was 600, the actual crime rate and the random forest prediction crime rate were 75.9% and 76.3%, respectively, and when the experimental sample was 600, the actual crime rate and the random forest prediction crime rate were 85.9% and 87.3%, respectively. The experiment results about association rules in this paper showed that with a sample size of 600, the correlation between the test sample and the experimental sample was observed to be 0.92 and 0.97, respectively. The experimental results indicated that the random forest algorithm demonstrated effective prediction abilities, with actual crime rates closely matching the predicted rates in both test and experimental samples. Similarly, association rules showed a strong correlation between the test and experimental samples, further validating the predictive capabilities of the algorithms. The findings underscore the effectiveness of random forest and association rules in predicting Internet financial crime, emphasizing the potential of computer algorithm combinations in crime prediction. The study highlights the importance of applying these algorithms to the legal regulation of Internet financial crime. Further research is proposed to delve deeper into the application of these algorithms in real-world scenarios and explore additional methods to enhance the prevention and management of Internet financial crime.
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