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The accurate evaluation of shale oil and gas reservoirs is of great significance to the integrated development of geology and engineering. Based on the core analysis, conventional logging, and array acoustic logging data, the total organic carbon, hydrocarbon generation potential, brittleness, and anisotropy of shale reservoirs were calculated. The p-wave time difference curves calculated by the artificial neural network (ANN) method and the conventional logging curve fitting method were compared. The multiresolution graph-based clustering (MRGC) method was used to classify shale reservoirs into three categories and evaluate the classification results. Brittle minerals such as quartz and feldspar were mainly found to be present in shale reservoirs and clay minerals which mainly consisted of illite. The Chang 73 reservoir is rich in organic matter and has great potential for survival. The p-wave time difference calculated by the fitting formula of the shear wave time difference meter demonstrated high accuracy and did not require a complex ANN model. MRGC method can well classify shale reservoir types. The classification results reduce the interference of human factors and are more scientific and reasonable. This research method is of great significance for the scientific classification and evaluation of shale reservoirs. [ABSTRACT FROM AUTHOR]