Female Identity and Class Imagination in Qing Chinese Export Paintings: An Artificial Intelligence-Assisted Study of Cross-Cultural Interpretation.

This study examined recurring representations of female identity and class-coded visual cues in a corpus of 433 Qing Chinese export paintings dated 1757-1911. The workflow combined CLIP ViT-B/32 for preliminary labeling, Segment Anything Model (SAM)-assisted localization, dimensionality reduction and clustering, and manual iconographic coding by two independent coders. CLIP labels were used for preliminary organization, SAM served as a localization aid, and adjudicated human coding provided the reference categories. The aggregate results identified seven human-reference categories: elite/beauty (n = 112), laboring (n = 72), domestic (n = 64), servant (n = 58), ritual/wedding (n = 45), market (n = 43), and performing (n = 39). Preliminary computational labels matched the human-reference labels for 356 of 433 paintings (82.2%), with category-level recall ranging from 73.3% to 88.4%. Recurring combinations of clothing, spatial setting, objects, posture, and depicted social relations were used to characterize the visual profiles of these categories. Evaluation of the 433 case-level AI-human label pairs yielded comprehensive category-level precision, recall, and F1 scores, alongside a confusion matrix detailing classification patterns. Cluster validation supported the selected clustering solution and demonstrated correspondence between the derived clusters and human-defined reference categories. Overall, recurrent combinations of clothing, spatial setting, objects, posture, and social relations indicate patterned pictorial typologies within the sampled corpus. These findings characterize visual representation in Qing Chinese export paintings but should not be interpreted as direct measures of legal status, lived experience, market demand, or historical foreign reception.