针对车辆重识别任务中由于车辆目标的类内差异性及类间相似性，导致特征感知能力不足，降低重识别准确度的问题，提出一种基于多属性自适应聚合网络架构（MaAPN）的车辆重识别方法。首先以ResNet-50网络为特征提取骨干网络，并通过引入IBN自适应模块提取域适应性强的特征表示；再将摄像机视角、车辆类型和车辆颜色等属性集成至网络中，构建多属性自注意特征强化模型，提升特征表示的鲁棒性与可辨别性；最后设计一种综合损失函数，通过优化样本间的特征距离，进一步提升网络模型的精度。实验结果表明，MaAPN架构在VeRi-776数据集与VERI-WILD数据集上平均精度分别达到了87.3%和86.9%，在各类评价指标上均取得了最优结果，有效提升了车辆重识别任务的准确度。

Aimed at the problem of insufficient feature perception ability and reduced recognition accuracy in vehicle reidentification tasks due to intra-class differences and inter-class similarity of vehicle targets, a vehicle reidentification method based on multi-attribute adaptive aggregation network architecture is proposed. Firstly, the ResNet-50 network is used as the backbone network for feature extraction, and the IBN adaptive module is introduced to extract feature representations with strong domain adaptability. Next, the attributes such as camera perspective, vehicle type, and vehicle color are integrated into the network, which is constructed a multi-attribute self-attention feature enhancement model to enhance the robustness and discriminability of feature representation. Finally, a comprehensive loss function is designed to further improve the accuracy of the network by optimizing the feature distance between samples. The experimental results show that the MaAPN architecture achieves an average accuracy of 87.3% and 86.9% on the VeRi-776 and VERI-WILD datasets respectively, and achieves optimal results on various evaluation indicators, effectively improving the accuracy of vehicle re identification tasks.