A state recognition method for fuselage join based on gradient boosting decision tree (GBDT) was proposed by considering the practical conditions of the lack of label and sample imbalance of fuselage joining data in order to monitor the process of fuselage join in real time. The displacement and the load data were acquired in real time through the positioners and the load sensors during the process of fuselage join. The joining state of historical data was labeled based on the process of airliner component join, and an accurate and efficient automatic labeling method for fuselage joining state was proposed. The state recognition model for fuselage join based on GBDT was trained through the labeled data, from which the importance of each feature was obtained. The macro_F₁ for joining state recognition of the proposed method was as high as 0.998, compared with the latest deep learning methods such as long short-term memory (LSTM), convolutional neural network (CNN) and some traditional machine learning methods. Each joining state was accurately recognized, and the model training process was more efficient.