The detection of abnormal ship behavior is a crucial aspect in maritime transportation and vessel supervision for ensuring maritime safety and enhancing operational monitoring efficiency. This study introduces a novel approach based on Fuzzy Chaotic Logistic Map to analyze and detect chaotic characteristics in ship behavior. Static and dynamic ship data, including latitude, longitude, Speed Over Ground (SOG), Course Over Ground (COG), and heading, are obtained from the Automatic Identification System (AIS) to assess abnormal behavior. Variations in speed and course between consecutive data points serve as inputs for the Ship Maneuvering Conditions Index (SMCI), whereas Cross-Track Distance (XTD) and deviation angle inform the Ship Operation Behavior Index (SOBI). Both indices feed into the Fuzzy Logic Control (FLC) system to generate an abnormality index. Through logistic maps and the 0-1 chaos test, bifurcation diagrams are constructed to visualize the effects of this abnormality index on ship behavior, enabling a comprehensive evaluation of stability, bifurcation, and chaotic states. Experimental results demonstrate that integrating fuzzy logic with chaotic mapping significantly enhances abnormal ship behavior detection and predictive capability in maritime monitoring. The 0–1 test effectively identifies system stability transitions from stable to chaotic states. By employing multivariable and multidimensional data analysis, this study offers a robust method for improving maritime traffic management and safety, thereby enhancing the accurate identification of abnormal ship behaviors.