Abstract: Bayesian method is an essential method in classification technology, Bayesian classifier is deduced by Bayesian rule and related properties.After introducing the attribute conditional independence assumption, we obtain the Naive Bayesian classifier, which reduces the computational complexity without reducing the performance of the classifier, and is simpler in practical application.Different probability density estimation methods have different effects on the conditional probability of each attribute, and also affect the performance of Naive Bayesian classifier.In this paper, we propose two kinds of probability density estimation methods:kernel density estimation and mixed Gaussian model.The two methods have their advantages and disadvantages, and we apply it to the example.We select the 89 logging logs from the ancient gas wells in the 41-33 block of Su Dong as the research data.We use the kernel density estimation and the mixed Gaussian modelto get the probability density estimation of the training data respectively, and the single Gaussian model is used as the contrastmethod.Then the Naive Bayesian method is used to classify the test data and the accuracy of the classification under different probability density estimation methods is calculated.