Accuracy and efficiency are key challenges in modern tax management. Traditional methods often face limitations when dealing with complex probabilistic relationships. In order to solve the above problems, this paper designs a tax AI system based on Bayesian network algorithm, and improves the accuracy of tax risk prediction and abnormal transaction detection based on the application of this network algorithm technology. The application data shows that after the application of this system, the accuracy of tax risk prediction and abnormal transaction detection reaches 92.52% and 85.14% respectively, and the false alarm rate is only 5.22%. In addition, the system significantly shortens the compliance check time to 10.05 h, reduces the error rate to 2.61%, and optimizes the tax declaration process based on policy impact analysis to improve taxpayer compliance. The data display includes prediction accuracy, false positive rate, check time, error rate, and user satisfaction. The results show that the tax AI system based on Bayesian network algorithm performs well in dealing with complex tax problems, and is a powerful tool to improve the efficiency and user experience of tax management.