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On Two Paradigms of Chinese Iconology Theory

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DOI: 10.23977/jipta.2023.060109 | Downloads: 13 | Views: 496


Zhang Tan 1


1 Southwest University, Chongqing, China

Corresponding Author

Zhang Tan


With the development of information technology and the arrival of the era of image reading, image research has increasingly attracted the attention of the humanities. Focusing on the interpretation of image data in historical periods, the study of image historiography in history and the study of iconography in art history are undoubtedly the two most important paradigms in current Chinese academic circles. Since the two paradigms belong to different disciplines, their application is either restricted in the field of history or art, and there is a lack of communication between the two. In view of this, this paper tries to make a systematic analysis of the two image theories, and to compare their advantages and disadvantages. The iconographic theory represented by Panofsky is more systematic and complete, as it has the advantages of focusing on themes, advancing in layers, and exploring functions; while the image research methodology of image historiography is still under exploration despite the fact that academic circles have attempted to construct the subjectivity of historical images, because it has been deeply influenced by the tradition of "proving history with pictures". Therefore, this paper argues that the iconographic theory of art history is more applicable. However, in the specific application of the iconography theory represented by Panofsky, Gombrich's situational logic theory should be introduced as a term of limitation to restrict the relationship between image themes, and effectively focus on the association of historical backgrounds, so as to be more consistent with historical reality. This demonstrates that the two paradigms of iconography above actually have the potential to converge.


Image History, Iconography, Panofsky, Gombrich


Zhang Tan, On Two Paradigms of Chinese Iconology Theory. Journal of Image Processing Theory and Applications (2023) Vol. 6: 80-85. DOI:


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