Painter cohort analysis has long been regarded as a key lens for studying how painting artistic styles develop and transmit across generations. Through a two-year collaboration with art historians, we identify key challenges in traditional painter cohort research: the unstructured characteristic of painter features, the entangled complexity of inheritance relationships, and the cognitively demanding nature of cohort definition and validation. To solve these challenges, we propose HPC-Vis, a visual analytics system for interactive exploration of historical painter cohorts. An improved cohort analytical workflow is designed to integrate structured feature construction, visualization-assisted exploration, algorithm-based recommendation, and unified cohort management. Based on this workflow, we develop three core computational modules: a multi-scale artistic feature construction method that leverages LLMs to extract and organize hierarchical style features from unstructured historical texts, an inheritance reconstruction algorithm that transforms the entangled multi-parent inheritance network into a clear hierarchical forest structure, and a recommendation model that identifies core features of the cohort and recommends cohort members via painter relevance assessment. To support smooth interactive exploration, we further design a set of novel visualizations with multidimensional collaboration, especially an inheriting mountain view inspired by traditional Chinese landscape paintings, and a foldable doughnut chart for hierarchical artistic style labels. HPC-Vis is evaluated and validated through case studies, user studies, and technical evaluations, demonstrating its effectiveness in supporting painter cohort exploration and in providing visual insights for art historical research.

翻译：画家群体分析长期被视为研究绘画艺术风格演化与代际传承的关键视角。通过与艺术史学家们开展为期两年的合作，我们识别出传统画家群体研究面临的核心挑战：画家特征的非结构化特性、传承关系的错综复杂性，以及群体定义与验证的高认知负荷难题。为应对这些挑战，我们提出HPC-Vis系统——一个面向历史画家群体交互式探索的可视化分析系统。研究设计了改进的群体分析工作流，整合了结构化特征构建、可视化辅助探索、算法推荐与统一群体管理四大模块。基于该工作流，我们开发了三个核心计算模块：基于大语言模型从非结构化历史文献中提取并组织层级化风格特征的多尺度艺术特征构建方法、将纠缠的多亲代传承网络转化为清晰层级森林结构的传承重构算法，以及通过画家相关性评估识别群体核心特征并推荐成员的推荐模型。为支撑流畅的交互式探索，我们进一步设计了一系列具有多维协同特性的创新可视化形式，特别包含受中国传统山水画启发的"传承山脉视图"与面向层级化艺术风格标签的可折叠环形图。通过案例研究、用户评估与技术验证，HPC-Vis的有效性在支撑画家群体探索及为艺术史研究提供可视化洞见方面得到充分验证。