Coal texture properties are one of important factors for determining the gas sorption capacity and transport properties. The recognition of coal texture with drilled cores or mining seam observation is direct and effective methods, but both methods are expensive and impossible for unexplored coal seams. The cut-off values logging data were applied in a few coal basins while the method ignores the effect of different coal ranks and is complicated with blurry boundary. In this study, 174 coal cores data obtained from 18 CBM wells were correlated with their geophysical logging responses in northwestern Guizhou Province. Four well-logging curves of the caliper logging (CAL), densities (DEN), natural gamma (GR) and deep lateral resistivity (LLD) were chosen to analyze coal textures. With progressive damage of coal, both values of CAL and LLD gradually increase while the DEN and GR tend to decrease. The identification index was reconstructed by using the principal component analysis (PCA) to identify coal texture of different coal ranks to improve the qualifies of coal texture identification and reduce multiple solutions. The logging evaluation method for coal texture identification were applied in multiple coal seams in western Guizhou Province to validate the prediction method of logging data. The results show that PCA is feasible tool to analyze coal texture with improving accuracy and the well logging identification coal texture is good consistency with core identification in different rank coal.