随着全球专利申请呈指数级增长,开展特定领域专利分析以挖掘专利数据潜在信息,寻找新质生产力和技术创新方向,日益受到国家、行业、研究机构的重视。然而,当前传统专利分析工具存在的静态分析单一、交互性不足、技术关联表达困难等问题日益凸显。聚焦专利关联度算法和D3.js可视化技术,构建基于动态网络图的专利分析技术方案,实现深度关联度挖掘、动态实时交互、海量数据处理、专利多维聚类分析等核心功能。铀矿专利可视化实例验证表明,底层算法分析数据准确、表达清晰、鲁棒性良好,动态网络图展现核心专利识别、技术分支分析等能力良好。该技术方案具有一定的实用价值和研究意义,有望推动专利分析从“静态统计”迈向“动态决策”的新阶段。

With the exponential growth of global patent application, conducting domain-specific patent analysis to uncover latent information in patent data, identify emerging productive forces, and explore technological innovation pathways has become a critical focus for national strategies, industrial sectors, and research institutions. However, current conventional patent analysis tools increasingly reveal significant limitations, including static analytical methods, insufficient interactivity, and difficulties in representing technological correlations. This study concentrates on patent relevance algorithms and D3.js visualization techniques to construct a patent analysis technical solution based on dynamic network graphs, achieving core functionalities such as deep correlation mining, dynamic real-time interaction, massive data processing, and multidimensional patent clustering analysis. Validation through a uranium mining patent visualization case demonstrates that the underlying algorithms exhibit high data accuracy, clear representation, and robust performance. The dynamic network graphs effectively visualize core patent identification, technology branch analysis. This technical solution demonstrates substantial practical value and research significance, potentially advancing patent analysis from "static statistics" to a new phase of "dynamic decision-making".