Microbial structures are developed in microbial carbonate rocks,with strong diagenesis superimposed,and their lithology-electrical property relationship is more complex.Conventional logging has been unable to distinguish microbial structure characteristics.Although electric imaging logging has high resolution and can identify microbial structures,there is also a problem of multiple solutions.At present,the combination of conventional logging and electrical imaging logging is the most effective and accurate identification method.The main methods of combination include chart method and artificial intelligence learning method.However,the efficiency of chart method is low,and artificial intelligence methods also have two problems:(1)there is difficulty in integrating logging data from different dimensions,(2)the core sampling data is limited,and the number of training samples is insufficient.Therefore,this article selects the K-Neighbor Classification Algorithm(KNN),a machine learning method that adapts to few samples,and proposes a method of separate training and recognition,and re-fusion of recognition results.Firstly,based on core data,we establish lithofacies classification schemes and rock structure feature classification schemes respectively,and establish a core training sample parameter library,and then use KNN method to identify lithofacies types with conventional logging and rock structure types with electrical imaging logging.Finally,based on expert experience,we fuse the two recognition results to obtain finely classified lithofacies types.Taking the Dengying Member 4 in the GM area of Sichuan Basin as an example,6 types of lithofacies and 7 types of rock structural feature types were identified.Based on expert experience fusion,9 types of finely classified lithofacies were finally identified,with a recognition accuracy rate over 85，.This study has effectively supported the fine research work on sedimentary microfacies of the Dengying Member 4 in the GM area and promoted the exploration and development work in Sichuan Basin.This method leverages the advantages of conventional logging and electrical imaging logging,achieving efficient and high-precision identification of lithofacies,and is worth promoting.