To further explore the influence path of internet finance on the risk prevention and management of commercial banks, the backpropagation neural network optimization algorithm was used to predict the risk value and the change of the risk level of commercial banks under the background of internet environment was empirically studied and analyzed. The results showed that the maximum size of genetic algebra and the number of individuals significantly impacted the algorithm’s optimization performance when the genetic algorithm was used for parameter optimization. Through continuous attempts, the prediction effect was the best when the genetic algebra was 62, and the individual number was 45. The training network showed that the test set’s fitting degree was 96.07%, and the prediction error was 0.84%, which was much better than those before optimization. When the predicted risk value was more significant than 0.39, the bank should be vigilant and strengthen risk prevention. The development of internet finance can reduce commercial banks’ business risk levels, reduce their dependence on traditional business, and decrease commercial banks’ business risk levels. It can be seen that commercial banks can effectively improve risk management ability and efficiency promoted by technological development, so the level of business risk they undertake can be reduced.

为了进一步探讨互联网金融对商业银行风险防范和管理的影响路径，采用反向传播神经网络优化算法预测风险价值，并以网络环境为背景，对商业银行风险水平的变化进行了实证研究。研究和分析。结果表明，当使用遗传算法进行参数优化时，遗传代数的最大大小和个体数显着影响算法的优化性能。通过连续尝试，当遗传代数为62，个体数为45时，预测效果最佳。训练网络表明，测试集的拟合度为96.07％，预测误差为0.84％，更好。比优化前的要高。当预测风险值大于0.39时，银行应保持警惕并加强风险防范。互联网金融的发展可以降低商业银行的业务风险水平，降低其对传统业务的依赖性，降低商业银行的业务风险水平。可以看出，商业银行可以有效地提高技术发展所促进的风险管理能力和效率，从而降低其承担的业务风险水平。