A Plithogenic Intelligence-Driven Approach to Analyzing Traditional Painting Techniques with Artificial Intelligence

Abstract

This paper introduces the Plithogenic Quality Evaluation Framework (PQEF), a 
novel extension of plithogenic sets and neutrosophic logic for evaluating traditional 
painting techniques. By integrating dynamic contradiction-adaptive aggregation, 
neutrosophic attribute value spectra, and a multi-dimensional quality index, the PQEF 
addresses the multi-attribute, contradictory, and uncertain nature of artistic quality 
assessment. Coupled with AI-driven feature extraction using convolutional neural 
networks and natural language processing, the framework offers a scalable and objective 
approach. A case study on Chinese ink wash paintings demonstrates superior accuracy ( 
R =0.92 ) compared to fuzzy (