Naive Bayesian classifier can be applied to lithologic identification. The Gaussian distribution is often used to fit the probability distribution of continuous attributes, but it is not effective for complex logging data. To solve this problem, a hybrid Gaussian probability density estimation based on EM algorithm is proposed. Logging data of the lower ancient gas Wells in the block 41-33 of Sudong are selected as training samples, and data of 44-45 Wells are selected as test samples. The experiment uses the mixed Gaussian model based on EM algorithm, to estimate the probability density of logging data variables at first, and then applies it to the Naive Bayes classifier for the lithology identification. Finally, the fitting effect of the single Gaussian distribution function was used as the comparison. The results reveal that the mixed Gaussian model has a better fitting effect and the performance of the Naive Bayes classifier for the lithology identification could be improved through this way.