The global landscape of art-technology institutions, including festivals, biennials, research labs, conferences, and hybrid organizations, has grown increasingly diverse, yet systematic frameworks for analyzing their multidimensional characteristics remain scarce. This paper proposes ARTLAS, a computational methodology combining an eight-axis conceptual framework (Curatorial Philosophy, Territorial Relation, Knowledge Production Mode, Institutional Genealogy, Temporal Orientation, Ecosystem Function, Audience Relation, and Disciplinary Positioning) with a text-embedding and clustering pipeline to map 78 cultural-technology institutions into a unified analytical space. Each institution is characterized through qualitative descriptions along the eight axes, encoded via E5-large-v2 sentence embeddings and quantized through a word-level codebook into TF-IDF feature vectors. Dimensionality reduction using UMAP, followed by agglomerative clustering (Average linkage, k=10), yields a composite score of 0.825, a silhouette coefficient of 0.803, and a Calinski-Harabasz index of 11,196. Non-negative matrix factorization extracts ten latent topics, and a neighbor-cluster entropy measure identifies boundary institutions bridging multiple thematic communities. An interactive web-based visualization tool built with React enables stakeholders to explore institutional similarities, thematic profiles, and cross-disciplinary connections. The results reveal coherent groupings such as an art-science hub cluster anchored by ZKM and ArtScience Museum, an innovation and industry cluster including Ars Electronica, transmediale, and Sonar, an ACM academic community cluster comprising TEI, DIS, and NIME, and an electronic music and media cluster including CTM Festival, MUTEK, and Sonic Acts. This work contributes a replicable, data-driven approach to institutional ecology in the cultural-technology sector.

翻译：全球艺术-科技机构（包括节日、双年展、研究实验室、会议及混合型组织）的格局日益多元化，但系统化分析其多维特征的理论框架仍十分匮乏。本文提出ARTLAS计算方法，将八轴概念框架（策展理念、地域关联、知识生产模式、机构谱系、时间取向、生态系统功能、受众关系与学科定位）与文本嵌入-聚类流程相结合，将78个文化-科技机构映射至统一的分析空间。各机构通过八个维度的定性描述进行表征，经E5-large-v2句子嵌入编码后，通过词级码本量化为TF-IDF特征向量。采用UMAP进行降维，结合凝聚聚类（平均连接法，k=10）获得0.825的综合评分、0.803的轮廓系数及11,196的Calinski-Harabasz指数。非负矩阵分解提取十个潜在主题，通过邻域-簇熵度量识别跨多个主题社区的边界机构。基于React构建的交互式网页可视化工具使利益相关者能够探索机构相似性、主题画像及跨学科关联。研究结果揭示了清晰的集群结构：以ZKM与ArtScience Museum为核心的艺术-科学枢纽集群，包含Ars Electronica、transmediale与Sonar的创新-产业集群，涵盖TEI、DIS与NIME的ACM学术社区集群，以及包含CTM Festival、MUTEK与Sonic Acts的电子音乐-媒体集群。本研究为文化-科技领域的机构生态研究提供了可复现的数据驱动型方法论。