Naive Bayesian classification algorithm is widely used in big data analysis and other fields because of its simple and fast algorithm structure. Aiming at the shortcomings of the naive Bayes classification algorithm, this paper uses feature weighting and Laplace calibration to improve it, and obtains the improved naive Bayes classification algorithm. Through numerical simulation, it is found that when the sample size is large, the accuracy of the improved naive Bayes classification algorithm is more than 99%, and it is very stable; when the sample attribute is less than 400 and the number of categories is less than 24, the accuracy of the improved naive Bayes classification algorithm is more than 95%. Through empirical research, it is found that the improved naive Bayes classification algorithm can greatly improve the correct rate of discrimination analysis from 49.5 to 92%. Through robustness analysis, the improved naive Bayes classification algorithm has higher accuracy.

朴素贝叶斯分类算法因其简单快速的算法结构被广泛应用于大数据分析等领域。针对朴素贝叶斯分类算法的不足，本文采用特征加权和拉普拉斯校准对其进行改进，得到改进的朴素贝叶斯分类算法。通过数值模拟发现，当样本量较大时，改进的朴素贝叶斯分类算法的准确率在99%以上，并且非常稳定；当样本属性小于400且类别数小于24时，改进的朴素贝叶斯分类算法的准确率在95%以上。通过实证研究，发现改进的朴素贝叶斯分类算法可以将判别分析的正确率从49.5%大幅度提高到92%。通过稳健性分析，改进的朴素贝叶斯分类算法具有更高的准确率。