随着智能网联汽车技术的迅猛发展，车辆不只是交通工具，还是集环境感知、计算与决策、数据交互于一体的移动智能终端。本文以大数据分析为基础，研究驾驶行为特征提取方法，构建多维度的驾驶行为评价体系，采用长短期记忆网络结合注意力机制（LSTM-Attention）深度学习模型开展行车风险预测，并采用真实的道路测试数据检验模型的风险预警是否有效。大数据驱动的驾驶行为分析可以很好地识别异常驾驶模式，显著提高主动安全系统预测能力，为智能网联汽车主动安全管理提供理论基础和技术支撑。

With the rapid development of intelligent connected vehicle technology, vehicles are no longer just means of transport; they have become mobile intelligent terminals that integrate environmental perception, computing and decision-making, and data interaction. Based on big data analysis, this paper studies the methods for extracting driving behavior characteristics, creates a multi-dimensional driving behavior evaluation system, and uses the LSTM-Attention deep learning model to predict driving risks. The model's risk warning effectiveness is verified using real road test data. Driving behavior analysis driven by big data can effectively identify abnormal driving patterns, significantly improve the predictive ability of active safety systems, and provide a theoretical basis and technical support for active safety management of intelligent connected vehicles.