To enhance the control performance and intelligent capabilities of dual clutch transmission vehicles while adapting to diverse driving behaviors and driving environments, this study proposes an integrated DCT vehicle control methodology that systematically incorporates driving behavior and environmental factors. The proposed framework comprises two core components: gear decision-making and clutch engagement control. For gear decision-making, an intelligent gearshift strategy based on naturalistic driving data and machine learning models is developed. This approach significantly improves the vehicle’s adaptability to driving behavior and environment while maintaining low fuel consumption. In clutch engagement control, focusing on the launch process, a real-time clutch engagement trajectory planning method and a data-driven launch predictive control strategy are proposed. These methods dynamically adapt to driver launch intentions and enhance tracking accuracy of the engagement trajectory. Simulation results demonstrate that the proposed intelligent DCT vehicle control methodology significantly enhances adaptability to driving behavior and operational environments, while improving overall vehicle performance and intelligent control capabilities.