[目的/意义]针对现有颠覆性技术识别侧重静态分析、难以连续刻画动态演化及精确量化变轨方向与爆发速率的局限，构建融合自然语言处理与几何动力学的颠覆性奇点识别计算模型，为科技前瞻与战略决策提供量化情报支撑。[方法/过程]利用SciBERT模型与滑动窗口技术，将离散专利文本映射至连续动态语义向量空间并提取技术质心；引入物理运动学理论，计算质心演化轨迹的切向加速度与法向变轨曲率，以表征研发资源聚集度与技术路线突变；结合核密度估计与数据长尾特征，以双重统计极值（Top10%）叠加为判据建立颠覆性奇点识别机制。[结果/结论]基因编辑领域实证显示，本模型成功捕获3次颠覆性范式跃迁的颠覆性奇点。较传统专利规模突变模型，可提前2—3年识别关键信号；较静态语义新颖度模型，能有效过滤常规微创新的伪突变噪音。研究实现了技术演化分析从静态计量到动力学刻画的方法论拓展，可为科技前瞻与战略决策提供情报支撑。

[Purpose/significance] To address the limitations of existing disruptive technology identification methods,which primarily rely on static analysis and struggle to continuously characterize dynamic evolution or accurately quantify the direction of trajectory shifts and breakout rates, this study constructs a disruptive singularity identification computational model integrating Natural Language Processing (NLP) and geometric dynamics, aiming to provide quantitative intelligence support for technological foresight and strategic decision-making. [Method/process] Utilizing the pretrained SciBERT model and sliding window techniques, discrete patent texts are mapped into a continuous, dynamic semantic vector space to extract the technological centroid. Drawing upon physical kinematics theory, the tangential acceleration and normal evolutionary curvature of the centroid's trajectory are calculated to represent the short-term concentration of R&D resources and technological route mutations, respectively. Furthermore, by combining Kernel Density Estimation (KDE) with the long-tail characteristics of the data, an identification mechanism for disruptive singularities is established using the superposition of dual statistical extremes (Top10%) as the strict criterion. [Result/conclusion] Empirical research in the gene-editing domain demonstrates that this model successfully captured the disuptive singularities associated with three transformative paradigm shifts. Compared to traditional patent volume mutation models, it can identify key signals 2-3 years in advance; compared to static semantic novelty models, it effectively filters out the pseudo-mutation noise of conventional incremental innovations. This study achieves a methodological expansion in technological evolution analysis from static scientometrics to dynamic kinematic characterization, providing robust intelligence support for technological foresight and strategic decision-making.