[目的/意义]培育新质生产力对精准研判科技创新发展态势提出新要求。汇交海量科技信息资源，深入追踪科技创新早期表征出的微弱现象和交互，对新兴技术向未来前沿、关键核心等技术演化的趋势进行预见，是科技发展规划的重要抓手。[方法/过程]开展关于技术预见分析的文献回顾，从新兴技术的演化机理和分析方法层面探讨新兴技术预见面临的主要问题，并结合新兴技术演化阶段研判与动态多重知识网络的表示学习等方法，构建弱信号视角下基于图学习的新兴技术预见框架。[结果/结论]该框架基于演化特征与表征要素的全周期分析与揭示，深入剖析新兴技术的演化机理，提出了基于图表示学习追踪建模新兴技术多元演化特征的详细步骤，对科技政策与管理领域有重要的理论与实践意义。

[Purpose/significance] Cultivating new quality productive forces presents new demands for accurately assessing the development trends of sci-tech innovation. It is an important tool for sci-tech development planning to pool
massive sci-tech information resources, deeply track the weak phenomena and interactions characterized in the early
stage of sci-tech innovation, and foresee the trends of emerging technologies evolving to frontier or core technologies.[Method/process] This study conducted a literature review on technology foresight analysis, explored key challenges in emerging technology foresight from the perspectives of evolutionary mechanisms and analytical methodologies, and constructed a graph learning-based emerging technology foresight framework from the weak signal perspective by integrating rules for judging the evolutionary stages of emerging technologies with representation learning from dynamic multiplex knowledge networks. [Result/conclusion] This framework employs a full-cycle analysis and revelation of evolutionary features and representational factors to deeply dissect the evolution mechanisms of emerging technologies.It proposes detailed steps for tracking and modeling the diverse evolutionary features of emerging technologies using dynamic multiplex knowledge networks, holding significant theoretical and practical implications for the field of sci-tech policy and management.