We present a proof-of-concept system that automates iconographic classification and content-based recommendation of digitized artworks using the Iconclass vocabulary and selected artificial intelligence methods. The prototype implements a four-stage workflow for classification and recommendation, which integrates YOLOv8 object detection with algorithmic mappings to Iconclass codes, rule-based inference for abstract meanings, and three complementary recommenders (hierarchical proximity, IDF-weighted overlap, and Jaccard similarity). Although more engineering is still needed, the evaluation demonstrates the potential of this solution: Iconclass-aware computer vision and recommendation methods can accelerate cataloging and enhance navigation in large heritage repositories. The key insight is to let computer vision propose visible elements and to use symbolic structures (Iconclass hierarchy) to reach meaning.

翻译：我们提出一个概念验证系统，该系统利用Iconclass词汇表和选定的人工智能方法，实现了数字化艺术作品的自动化图像学分类与基于内容推荐。该原型系统实施了四阶段工作流用于分类与推荐，整合了YOLOv8目标检测与Iconclass编码的算法映射、基于规则的抽象意义推理，以及三种互补的推荐器（层次邻近度、IDF加权重叠度和Jaccard相似度）。尽管仍需更多工程优化，评估结果表明该方案具有潜力：具备Iconclass意识的计算机视觉与推荐方法能够加速大型文化遗产库的编目流程并增强导航体验。核心思路是让计算机视觉识别可见元素，并利用符号化结构（Iconclass层次体系）来解析意义。