Ship collision avoidance decision-making is a core determinant of navigational safety, and its effectiveness directly governs a ship’s ability to operate safely in a complex environment. Although successive editions of the COLREGs have provided a relatively systematic qualitative description of the principal encounter state and corresponding handling principles, they still lack a quantitative specification of collision avoidance actions. As a result, ship officers must rely heavily on experiential judgment for state recognition and decision-making in real operations, which in turn increases the likelihood of human error-induced failures in avoidance. To address this problem, this study constructs a COLREG-compliant collision avoidance decision azimuth map derived from the collective wisdom of ship officers. Specifically, a two-stage mining process tailored to large-scale AIS data is designed: in the first stage, ship–ship encounter cases are extracted from the full dataset, and, in the second stage, collision avoidance actions are mined from these encounter cases. Subsequently, a decision tree classification model is employed to partition the latent relationships between the relative motion features of two ships and their collision avoidance actions under different encounter scenarios, thereby constructing a data-driven ship collision avoidance decision azimuth map. Finally, taking the eastern coastal waters of China as a case study, the constructed ship collision avoidance action azimuth map is shown to provide scenario-specific guidance for two-ship encounters, offer an objective basis for the quantitative enrichment of COLREGs, and supply a methodological reference for future autonomous ship collision avoidance systems.