The accurate recognition of segment joints serves as a critical step for capturing joint anomaly information, evaluating segment assembly quality, diagnosing structural health status, and determining the loosening of connecting bolts. It holds significant importance for the operation and maintenance of shield tunnels. However, existing studies on joint recognition based on Rail-borne Mobile Laser Scanning (RMLS) suffer from insufficient comprehensiveness in feature representation, leading to notably poor accuracy and robustness under complex scenarios such as noise interference, data loss due to object occlusion, and uneven point cloud density. To address this issue, this study proposes a shield tunnel segment joint recognition method based on the Joint Composite Feature Index (JCFI). The proposed method first employs a cross-sectional ellipse fitting approach to filter out obvious non-lining points. Subsequently, a composite index JCFI, which integrates curvature, left-right density ratio, and relative depth, is designed to quantitatively characterize the feature differences of segment joints. Finally, based on the constructed JCFI indicator, the recognition of circumferential and longitudinal joints is sequentially achieved. Validation tests using RMLS point cloud data from the Guangzhou Metro Line 8 tunnel demonstrate that the proposed method, by constructing the JCFI that comprehensively characterizes joint features, effectively handles complex scenarios including noise interference, joint missing, and uneven point cloud density. The joint recognition achieves a recall rate of 90.14%, a precision rate of 99.04%, and an IoU of 89.36%, providing a reliable technical solution for the accurate identification of shield tunnel segment joints.