With the development of intelligent waterborne transportation, mining collision avoidance patterns based on spatiotemporal and motion data of ships are crucial for the autonomous navigation of intelligent ships, which requires accurate collision avoidance information under various encounter scenarios. Addressing the existing issues of low precision and false detection in data mining algorithms, this paper proposes a collision avoidance behavior mining model considering encounter scenarios. The model is based on the Automatic Identification System (AIS) and the International Regulations for Preventing Collisions at Sea (COLREGs); it firstly identifies ship collision avoidance turning points by analyzing trajectory curvature with turning and recovering factors. Then, by combining AIS data and the specific navigational environment, it matches the ship encounter pairs and determines the encounter scenarios. Comparative experiments show that the model demonstrates superior accuracy in various scenarios compared to traditional algorithm. Finally, the model was applied to AIS data east of the Yangtze River Estuary, recognizing a total of 827 instances of ship collision avoidance behavior under different encounter scenarios. The case study shows that the model can precisely mine collision avoidance information, laying a solid foundation for future research on autonomous collision avoidance decision making for intelligent ships.