针对车辆变道插入后目标车道后方车队微观交互模式时空演化规律刻画不足的问题，本文提出了一种基于交互强度原语识别的车队时空演化分析方法。首先，提取车辆变道插入后的后方受影响车队轨迹数据，引入安全势能场理论，综合考虑车辆间的冲突严重程度与时空接近特性，建立了车辆间交互强度计算方法；其次，结合层次狄利克雷过程-隐马尔可夫模型（HDP-HMM），实现了在无须预设状态数的情况下对交互强度时间序列中原语识别；随后，利用聚类算法确定交互强度阈值并定义了不同风险等级的交互模式，并分析了车队内交互模式的传播与转移特性。最后，基于CitySim数据集选取了1,763个变道事件进行实证分析。结果表明：研究共识别出16,550个交互原语，并提取出11类典型交互模式；交互原语的平均持续时间为1.90秒，符合驾驶人反应时间。在演化特征方面，中风险保持模式占比最高（31.29%），高风险上升模式占比最低（4.48%）；车队内部表现出明显的风险后向传播特征与自转移稳定性，其中中风险保持模式的自转移频率最高。本研究构建的原语-模式分析框架揭示了变道扰动下队内车辆交互强度的演化规律，可为自动驾驶变道决策优化、交通风险实时预警及微观仿真模型标定提供理论支撑。

To address the inadequate characterization of the spatiotemporal evolution of microscopic interaction patterns in platoons following lane-changing insertions, this paper proposes a method for analyzing platoon evolution based on the identification of interaction intensity primitives. First, vehicle trajectory data from the affected platoon behind a lane-changing vehicle are extracted. By integrating safety potential field theory with considerations of conflict severity and spatiotemporal proximity, a quantitative model for vehicle interaction intensity is established. Second, a Hierarchical Dirichlet Process-Hidden Markov Model (HDP-HMM) is employed to automatically identify primitives within interaction intensity time series without pre-specifying the number of states. Subsequently, clustering algorithms are utilized to determine interaction intensity thresholds and define interaction patterns across different risk levels, followed by an analysis of the propagation and transition characteristics within the platoon. Finally, an empirical analysis is conducted using 1,763 lane-change events from the CitySim dataset. The results indicate that 16,550 interaction primitives were identified, from which 11 typical interaction patterns were extracted. The average duration of these primitives is 1.90 seconds, consistent with human driver reaction times. In terms of evolutionary characteristics, the middle-risk maintain pattern has the highest proportion (31.29%), while the high-risk rising pattern has the lowest (4.48%). Significant backward risk propagation and self-transition stability are observed within the platoon, with the middle-risk maintain pattern exhibiting the highest self-transition frequency. The primitive-pattern framework developed in this study uncovers the evolution laws of vehicle interaction intensity under lane-changing perturbations, providing theoretical support for autonomous driving decision-making, real-time traffic risk warnings, and the calibration of microscopic simulation models.