为提升环形交叉口等复杂结构化场景下的车辆轨迹预测精度，提出一种名为MST (MHA-SGC-TimeMixer)的深度学习框架，该框架构建了一个宏观-微观双层编码体系。宏观层面，采用多头注意力机制(Multi-Head Attention, MHA)捕捉由全局道路拓扑施加的长时程战略约束，即通过建模车辆完整历史轨迹与环岛结构，如出入口的深层关系，来推断其长期行驶意图；微观层面，首先利用简化图卷积网络(Simplified Graph Convolution, SGC)提取车辆间的即时空间关系，随后引入TimeMixer机制，将一维交互时序映射为多尺度、多分辨率的二维时空图像，通过对周期性战术行为与趋势性战略意图的显式解耦和分层融合，实现对深层交互模式的精准捕捉。两类信息经门控网络融合后，由门控循环单元(Gated Recurrent Unit, GRU)解码器生成最终轨迹。在公开数据集INTERACTION及RounD上的实验结果表明，在5秒预测时域内，所提模型在INTERACTION数据集上的平均位移误差(Average Displacement Error, ADE)与最终位移误差(Final Displacement Error, FDE)分别为1.19米和1.85米，在RounD数据集上分别为1.16米和1.80米，均优于对比基线模型。研究表明，通过对宏观全局约束与微观时空交互进行分层建模，特别是对交互模式进行解耦分析，能有效提升模型在复杂场景下的轨迹预测性能。

To enhance vehicle trajectory prediction accuracy in complex structured scenarios such as roundabouts, a deep learning framework, namely MST (MHA-SGC-TimeMixer), is proposed. The framework is built upon a macro-micro dual-encoder architecture. At the macro level, a Multi-Head Attention (MHA) mechanism is employed to capture the long-term guiding constraints between vehicles and the global road topology. At the micro level, a Simplified Graph Convolutional Network (SGC) first extracts instantaneous spatial relationships among vehicles. Subsequently, the TimeMixer mechanism is introduced to map the one-dimensional interaction sequence into multi-scale, multi-resolution 2D spatio-temporal images. By explicitly decoupling and hierarchically fusing periodic tactical behaviors and trending strategic intentions, a precise capture of deep interaction patterns is achieved. The information streams from both levels are integrated via a gated fusion network and then fed into a Gated Recurrent Unit (GRU) decoder to generate the final trajectory. Experiments on the public INTERACTION and RounD datasets demonstrate the framework's effectiveness. Within a 5-second prediction horizon, the proposed model achieves an Average Displacement Error (ADE) and a Final Displacement Error (FDE) of 1.19m and 1.85m on the INTERACTION dataset, and 1.16m and 1.80m on the RounD dataset, respectively, outperforming all baseline models. The results indicate that hierarchically modeling macro-level global constraints and micro-level spatio-temporal interactions, particularly through the decoupling analysis of interaction patterns, can significantly improve trajectory prediction performance in complex scenarios.