Due to the continuous characteristics of logging data tags, data samples have strong contextual relevance. Aiming at the problem that the basic identification unit constructed by the existing lithology identification methods cannot make full use of the context information provided by the continuity of logging curves, a lithology identification method based on hierarchical clustering meta-object representation is proposed. This method is based on the hierarchical clustering method based on regional growth, comprehensively using multiple conventional logging curves to automatically stratify the target reservoir, and then realize the complete characterization of the meta-object from the perspective of statistical and morphological features. After the features are extracted Lithology identification is performed on the rich feature space formed. Through the comparative experiment of lithology identification with actual logging data in Daqing Oilfield, the performance of various lithology identification of the experimental group using the proposed method has been significantly improved.