The inherent complexity of traffic patterns in inland river tributary waterways presents significant challenges in predicting ship behavior, resulting in elevated accident risks compared to general waterways. With the intelligent development of inland navigation, conducting research on autonomous navigation decision-making for tributary waterway ships is crucial to improving navigation safety and efficiency. Based on the characteristics of the navigation environment, a digital traffic environment model for inland waterways with tributaries is constructed to meet the information requirements of autonomous navigation decision-making. The ship encounter process is analyzed, and a collision risk identification model based on trajectory derivation is proposed, which accounts for the uncertainty of ship maneuvering in tributary waterways. Under the premise of compliance with the “Rules of the People’s Republic of China for Inland River Collision Avoidance” (RPRCIRCA) and adherence to good seamanship, an autonomous navigation decision-making method is developed by integrating an improved Line-of-Sight tracking model with a collision avoidance strategy based on exhaustive course-speed maneuver combinations. The system’s performance is validated through simulation experiments, with trajectory tracking deviations demonstrated to remain below 49 m under wind-current disturbances while minimum encounter distances with target ships are maintained above 48 m. Adaptive response capabilities to maneuvering variations of target ships are confirmed, along with the preservation of navigation precision in complex tributary environments.