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 ASTRA (Art-technology Institution Spatial Taxonomy and Relational Analysis), 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 11196. Non-negative matrix factorization extracts ten latent topics, and a neighbor-cluster entropy measure identifies boundary institutions bridging multiple thematic communities. An interactive React-based tool enables curators, researchers, and policymakers to explore institutional similarities and cross-disciplinary connections. 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 cluster comprising TEI, DIS, and NIME, and an electronic music cluster including CTM Festival, MUTEK, and Sonic Acts. Code and data: https://github.com/joonhyungbae/astra

翻译：全球范围内的艺术-科技机构图景——包括艺术节、双年展、研究实验室、会议及混合型组织——日益多元化，然而针对其多维特征的系统性分析框架仍较为匮乏。本文提出ASTRA（艺术-科技机构空间分类与关系分析）方法，这是一种计算性方法论，将八轴概念框架（策展哲学、地域关系、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）与文本嵌入及聚类流程相结合，将78个文化与科技机构映射至统一分析空间。每个机构通过沿八轴进行的定性描述加以特征化，经由E5-large-v2句子嵌入编码，并通过词级码本量化生成TF-IDF特征向量。采用UMAP进行降维后，通过凝聚式聚类（平均链接法，k=10）得到综合得分为0.825、轮廓系数为0.803、Calinski-Harabasz指数为11196的结果。非负矩阵分解提取出十个潜在主题，邻域-簇熵度量识别出连接多个主题社区的边界机构。一款基于React的交互式工具可供策展人、研究人员及政策制定者探索机构相似性及跨学科关联。结果揭示了若干具有一致性的分组，例如以ZKM与艺术科学博物馆为核心的艺术-科学中心聚类，包含Ars Electronica、transmediale及Sonar的创新与产业聚类，涵盖TEI、DIS及NIME的ACM学术聚类，以及包括CTM Festival、MUTEK与Sonic Acts的电子音乐聚类。代码与数据：https://github.com/joonhyungbae/astra