From the viewpoint of chemical composition categorization and structure classification of rocks, an effective method was proposed to identify the lithology of volcanic rocks by using logging data. On the one hand, the conventional logging data could be obtained by core wafer identification. Thus, after processing the data with Support Vector Machines (SVM) method of statistical theory, we could get the lithologic type of the volcanic rocks, which are classified according to the chemical composition of rocks. On the other hand, the volcanic rocks can be classified as volcanic lava, pyroclastic lava and pyroclastic rock according to the rock structure. Typical formation micro-resistivity imaging logging (FMI) image mode can be concluded by establishing the corresponding relationship between FMI images and lithology of volcanic rocks with different structures. As a result, the lithologic type of the volcanic rock classified by rock structure can be determined. Finally, by combining these two kinds of lithology, the ultimate rock lithology can be determined, too. In this paper, the authors presented a novel method to identify the lithology of volcanic rocks by combining SVM processed logging data and FMI image mode.