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A new ontology learning model called domain ontology graph (DOG) is proposed in this paper. There are two key components in the DOG, i.e., the definition of the ontology graph and the ontology learning process. The former defines the ontology and knowledge conceptualization model from the domain-specific text documents; the latter offers the necessary method of semiautomatic domain ontology learning and generates the corresponding ontology graphs. Two kinds of ontological operations are also defined based on the proposed DOG, i.e., document ontology graph generation and ontology-graph-based text classification. The simulation studies focused upon Chinese text data are used to demonstrate the potential effectiveness of our proposed strategy. This is accomplished by generating DOGs to represent the domain knowledge and conducting the text classifications based on the generated ontology graph. The experimental results show that the new method can produce significantly better classification accuracy (e.g., with 92.3% in f-measure) compared with other methods (such as 86.8% in f-measure for the term-frequency-inverse-document-frequency approach). The high performance demonstrates that our presented ontological operations based on the ontology graph knowledge model are effectively developed.