In recent years, with the emergence of autonomous racing competitions such as Roborace, autonomous racing cars have become a prominent research focus and have undergone rapid development. This paper establishes a reinforcement learning-based decision-making and control framework for autonomous racing cars, achieving cooperative optimization between high-speed racing and safety control under extreme operating conditions. By integrating a Control Barrier Function constrained reward function for dynamic stability and a racing line-guided path optimization, the proposed framework achieves a synergistic balance between minimum lap time and operational safety. Dynamic stability domain constraints based on a two-degree-of-freedom single-mass rigid-body model and racing line optimization based on track geometric modeling are established. The phase plane stability boundary is constructed using the yaw rate method and double-line method, upon which Control Barrier Function constraints are developed. A reward function considering dynamic stability domains is designed to ensure safety control during reinforcement learning. Simultaneously, a racing line optimization method based on track geometric modeling is proposed, generating four racing lines: shortest path, minimum time, minimum curvature, and minimum curvature (iterative). Incorporating curriculum learning concepts, the training process is divided into low-speed guidance and high-speed exploration stages with distinct reward functions to guide motion planning, ultimately achieving the minimum lap time objective. The proposed method is validated through simulation experiments. Comparative analysis is performed between the baseline algorithm, the racing line-guided algorithm, the dynamic stability domain-constrained algorithm, and the proposed algorithm. Evaluation metrics including lap time and average lap speed demonstrate that the proposed algorithm achieves higher speeds and safer driving behaviors compared to the baseline. Specifically, the proposed algorithm reduces lap time by 5.37%, increases average lap speed by 6.36%, and decreases yaw rate and sideslip angle constraint violations by 10.21% and 3.20% respectively, demonstrating effective balance between racing capability and safety constraints.