The geological conditions of deep coalbed methane are special, and the characteristics of coal differ greatly from those of medium and shallow layers. The classification and evaluation of coal quality logging are not yet complete, and it is urgent to carry out evaluation to help select favorable areas for deep coalbed methane and deploy well locations. Guided by the idea of multi factor and multi perspective analysis, a systematic logging evaluation of coal rock quality parameters such as macroscopic coal rock types, industrial components of coal, microscopic components of coal rock, vitrinite reflectance, and coal structure was carried out. Based on the fuzzy C-means algorithm(FCM) fuzzy clustering algorithm, a deep coalbed methane coal rock quality logging evaluation model was established, and the deep coalbed methane coal rock quality of 93 wells in Suide area was classified and evaluated using this method. The research results indicate that coal rock types, industrial components of coal, coal rock microscopic components, vitrinite reflectance, coal body structure, etc. can effectively reflect the coal rock quality of deep coalbed methane. The FCM fuzzy clustering algorithm can effectively identify the quality types of coal in the research area. Most of the well areas in the research area have good coal quality, and are distributed in contiguous areas. Only the coal quality in the northern Mi 161 and northeastern Yu 11 well areas is poor. The actual fracturing conditions and gas testing results reveal that the constructed deep coalbed methane coal rock quality logging evaluation method has strong applicability and can provide logging technology support for the evaluation of favorable areas for deep coalbed methane and well location deployment.