基于深度学习的《园林植物学》知识图谱构建与教学研究
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教育部国家级一流本科课程“种植设计”(编号:2023250077);上海市教委上海市重点课程“园林植物学”(编号:沪教委高[2024]38号);上海市哲学社会科学规划课题“韧性城市下基于社会—生态视角的长三角绿色基础设施优化研究”(编号:2021ECK002)


Deep Learning-Based Knowledge Graph Construction for Landscape Plants and Its Educational Applications
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    聚焦数智化时代对课程知识体系的重构需求,基于风景园林专业植物学课程现状与痛点,解析数智技术带来的变革契机,提出面向教学场景的园林植物知识图谱构建方法。以《园林植物学》教材为研究载体,通过“数据采集—实体抽取—图谱可视化”等知识图谱框架,建立涵盖“形态特征—生态习性—景观功能”多维属性的知识网络。整合教材中分散的756个植物实体与属性关系,形成可视化知识图谱。最后分析植物图谱构建结果及教学应用,并从智能导学系统、智能问答机器人、现有平台增强等方面展望知识图谱技术在课程改革中的延伸应用,为风景园林专业数智化教学转型提供参考。

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    The development in the digital intelligence era necessitates the restructuring of course knowledge systems. Based on the current status and challenges within botany courses for landscape architecture majors, this paper analyzes opportunities for transformation facilitated by digital-intelligent technologies. It proposes a pedagogically oriented approach for constructing a knowledge graph of landscape plants. We utilize the textbook Landscape Plants Science as a case study. By constructing datasets, extracting entities, and visualizing knowledge, we create a multidimensional knowledge network encompassing “morphological traits - ecological habits - landscape functions.” The approach successfully integrates 756 scattered plant entities and their attributes into a visual knowledge graph. Finally, we analyze the construction outcomes of the landscape plants knowledge graph and its pedagogical implementations, and explore applications such as smart tutoring systems, AI Q&A assistants, and platform enhancements for curriculum reform, providing practical references for digital-intelligent teaching transformation in landscape architecture education.

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  • 在线发布日期: 2025-10-11
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