[Objective] This paper creates an early warning mechanism for international journals, aiming to predict their quality changes and help researchers choose better publishing platforms. [Methods] We constructed an early-warning index system for scholarly journals with their impact strength, influencing timeline, characteristics, and author demographics. Then, we combined Pearson correlation coefficient and the important values of XGBoost to select features. Third, we analyzed the features with XGBoost, SVM, logistic regression, and Stacking fusion to identify the abnormal behaviors. Finally, we ranked these features with XGBoost information gain. [Results] We examined our method with three sample datasets from medical and scientific journals. The generalization of the model could be improved with feature screening, which could also slightly reduce the early warning performance. Feature screening and expansion could improve the accuracy of the early warning model. The self-citation and submission acceptance rates play significant roles for the model. [Limitations] Due to the actual acquisition of data, the range of disciplines involved is small and the training data is small, and journal features related to article processing charge are not included. [Conclusions] The proposed model could help institutions and researchers improve decision making on the quality of international journals.