The researchers improved the naive Bayes classification algorithm for managing traffic risks. They used feature weighting and Laplace calibration to enhance the algorithm's accuracy. The improved algorithm achieved over 99% accuracy with large sample sizes and was stable. For samples with fewer than 400 attributes and less than 24 categories, the accuracy was over 95%. The algorithm significantly increased the correct rate of discrimination analysis from 49.5% to 92% in empirical research. Additionally, it showed higher accuracy in robustness analysis.