Understanding and responding to driver behavior is significant for improving road traffic safety, optimizing energy efficiency, and enhancing ride comfort, especially in electric vehicles. This thesis proposes a human-machine interface (HMI) system framework that recognizes multiple types of abnormal driving behaviors in real time and provides personalized and immediate feedback to drivers through an intuitive interface. First, typical roads and traffic scenes are selected using the SCANeR Studio virtual driving simulation platform, allowing realistic driving simulation and data acquisition. Dynamic parameters such as vehicle speed, longitudinal and lateral acceleration, steering angle, pedal position, and motor efficiency are collected. The K-means algorithm is applied for preliminary data segmentation, then the improved DBSCAN (iterative DBSCAN) algorithm is used for unsupervised clustering to identify potential aggressive driving behaviors.