本研究聚焦电动汽车充电接口电气接触可靠性提升与故障预测模型构建。通过分析接触失效机理，提出线束模块化设计、抗腐蚀涂层应用等可靠性提升方案，并基于国际电工委员会（International Electrotechnical Commission，IEC）标准建立接触电阻动态监测方法。构建图卷积神经网络-长短期记忆网络（Graph Convolutional Neural Network-Long Short-Term Memory，GCN-LSTM）深度学习模型，融合电流 / 电压时序数据与用户行为特征，实现故障提前预警。实验验证表明，该模型在充电桩故障诊断中准确率达 88.12%，F1 分数（Score）为 0.844，性能优于传统模型。本研究为充电设施智能运维提供了理论支撑与技术路径，对保障新能源汽车产业健康发展具有重要意义。

This study focuses on enhancing the electrical contact reliability of electric vehicle charging interfaces and constructing a fault prediction model. By analyzing the failure mechanisms of electrical contacts, reliability improvement schemes such as modular wiring harness design and anti-corrosion coating application are proposed. A dynamic monitoring method for contact resistance is established based on IEC standards. A GCN-LSTM deep learning model is constructed to integrate current/voltage time-series data with user behavior features, enabling early fault warning.Experimental validation demonstrates that the model achieves an accuracy of 88.12% and an F1 Score of 0.844 in charging pile fault diagnosis, outperforming traditional models. This research provides theoretical support and technical pathways for intelligent operation and maintenance of charging facilities, which is of great significance for ensuring the healthy development of the new energy vehicle industry.