基于文本挖掘的扎染研究进展与热点分析OA
Research progress and hotspot analysis of tie-dye based on text mining
为系统解析扎染研究的发展脉络与内在规律,本研究以2000-2024年CNKI数据库文献为对象,综合运用LDA模型与主题热度指标进行文本挖掘分析.研究表明,扎染研究正处于快速发展阶段;在战略政策、消费需求、技术变革等驱动下,主要围绕非遗保护与活态传承、服饰与产品创新设计、地域资源与产业发展、纹样艺术与图案设计、技术工艺与材料创新、课堂美育与社区实践等6大主题维度演化进展,呈现多学科融合与技术应用创新的发展趋势;研究热点以"本体认知-应用实践-融合创新"阶段性跃迁,当前聚焦文旅产品创新、数字保护传播、区域协同发展3大前沿热点.未来研究将着力文化叙事、技术融合、全球协同网络等方向,构建扎染文化传承与时代创新的共生生态.
To systematically analyze the evolutionary trajectory and underlying patterns of tie-dye research,this study utilizes literature from the CNKI database(2000 to 2024)as its research subject.A comprehensive text mining analy-sis was conducted,integrating the LDA model and thematic heat metrics.The findings indicate that tie-dye research is cur-rently in a phase of rapid development.Driven by strategic policies,consumer demands,and technological advancements,the field has evolved primarily around six thematic dimensions:intangible cultural heritage protection and living inheri-tance,apparels and products innovation design,regional resources and industrial development,pattern art and design,tech-nical processes and material innovation,and classroom aesthetic education and community practice.This evolution demon-strates a trend towards multidisciplinary integration and technological application innovation.Research hotspots have under-gone stage-based transitions from"ontological cognition"to"applied practice"and then to"integrated innovation".Cur-rent research focuses on three cutting-edge hotspots:cultural tourism product innovation,digital preservation and dissemi-nation,and regional collaborative development.Future research will concentrate on cultural narrative construction,techno-logical convergence,and global collaborative networks,aiming to build a symbiotic ecosystem for the inheritance of tie-dye culture and its contemporary innovation.
方蕾蕾;吴巧英;郭紫晴;卫艺林
浙江科技大学艺术设计与服装学院,浙江 杭州 310023浙江理工大学马兰戈尼时尚设计学院(国际时装技术学院),浙江 杭州 310018浙江科技大学艺术设计与服装学院,浙江 杭州 310023浙江科技大学艺术设计与服装学院,浙江 杭州 310023
轻工纺织
扎染文本挖掘LDA模型主题识别主题演化热点主题主题热度
tie-dyetext miningLDA modeltopic identificationtopic evolutionhot topicstopic hotness
《染整技术》 2026 (4)
6-13,8
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