Traditional risk assessment methods mostly rely on deterministic indicators, making it hard to dynamically quantify risks from the uncertainty of surrounding vehicle behaviors. To address this limitation, this study proposes a risk assessment approach integrating information entropy-based uncertainty quantification and driving style. Specifically, information entropy quantifies the uncertainty of vehicle motion states, while different driving styles are converted into quantifiable risk values for driving risk evaluation. Firstly, an information entropy-based method assesses the uncertainty of vehicle motion states for real-time risk perception. Secondly, K-means clustering is applied to vehicle speed, jerk, and time headway to identify diverse driving styles. A weighted mechanism is then established to fuse driving style and information entropy for the proposed risk assessment model. Finally, experiments are conducted under congested and highway scenarios. The correlation between evaluated risks and actual vehicle energy consumption is analyzed, and comparisons are made with the artificial potential field method, deterministic risk assessment method, and learning-based risk assessment method. Results show that the proposed method reliably detects risks in various events. In terms of response time, it is reduced by an average of 50.55%, 86.35%, and 81.85% compared with the other three methods, respectively. Meanwhile, its computation time is significantly lower than that of the artificial potential field method and the learning-based risk method, demonstrating the effectiveness and application value of the proposed method in driving risk assessment.