At present, finite element modeling or laboratory testing methods are generally used in the research of the overall stability of high-strength steel members. However, the prediction method based on machine learning (ML) has greatly improved the accuracy and convenience of component performance prediction. To accurately predict the overall stability of welded constant section box columns made of high strength steel, ML method together with a database based on the fiber model is proposed in this paper. Firstly, the input and output parameters of the model are determined, and the database is provided. Then, three different ML models and empirical models in the existing specifications are selected for prediction, and the performance is compared according to the evaluation index. Finally, the rationality of ML models is analyzed according to interpretable algorithms. The results show that the prediction results of most ML models are in good agreement with the experimental results, which are slightly higher than the empirical models, and the Gaussian process regression model has the best prediction performance for the overall stability of high-strength steel members; the influential trend of various parameters on the overall stability of components meets the expectation, which verifies the rationality and reliability of the ML model; the regularized slenderness ratio has the greatest influence on the prediction results, while the initial defects have the least.