The development of autonomous vehicles is confronted with a significant challenge: dealing with complex driving scenarios affected by environmental, road, and traffic factors, which result in unpredictable real-world driving conditions. This paper is dedicated to the generation of an ordered scenario library for intelligent vehicles within such complex road environments. This study puts forward a novel driving scenario library generation framework. The proposed framework defines key scenario elements, calculates scenario importance indicators through the analysis of complexity and occurrence probability, constructs a scenario library, and assesses its diversity. The framework overcomes the limitations in scalability and the ability to model complex dynamic environments, enhancing the precision and flexibility of scenario generation. In essence, it provides a more effective approach to group scenarios and improves the testing efficiency of autonomous driving systems.