The machine learning method is the main technical means of carbonate lithofacies log identification. Selecting the appropriate machine learning method according to the different geological conditions and data is one of the key factors for high-precision identification of lithofacies. However, there are few researches on the applicability of machine learning identification methods. In this paper, four most commonly used machine learning methods for identifying lithofacies are studied, including Self Organizing Maps（SOM）, Multi-Resolution Graph-based Clustering（MRGC）, K Nearest Neighbor（KNN）, and Artificial Neural Network（ANN）. By comparing the principle and practical application effects of these methods, the advantages, disadvantages and applicability of the four machine learning methods have been summarized. When there are few core samples, MRGC is preferred, while when there are more core data, KNN is preferred as well as MRGC. Their application of lithofacies identification in the Longwangmiao Formation in the MX area in Sichuan Basin shows that MRGC and KNN are the best, SOM is the second, and ANN is the worst. This study of the application effects of machine learning methods provides a guidance for the identification of carbonate rock facies in other layers and regions, and has strong practical value.