Foreign-related rule-of-law personnel are an indispensable strategic resource for maintaining and expanding national interests, participating in the change of the international governance system, and providing important legal support for promoting the construction of a community of human destiny. This paper begins by outlining the objectives and strategies of training the foreign rule of law talents, describing the specific qualities of these talents, and establishing a “three-inone” foreign rule of law talents training model that integrates emerging information technology. The online learning big data platform for foreign rule of law talents provides the learning behavior data, which we weigh, process, and combine with the SHAP interpretable model to extract the learning behavior features. We used SMOTE to process data sampling imbalances and established a model for predicting student learning performance in combination with the XGBoost model. Additionally, we designed a comparative teaching experiment to analyze the teaching effect using the talent cultivation model. The SMOTE-XGBoost-SBD model yielded a coefficient of determination of 0.487, demonstrated a strong correlation between the predicted and actual performance, and yielded a SHAP value of 1.492 for the knowledge point mastery (C) characteristics of the foreign rule of law students. At the end of the teaching experiment, the mean value of the student’s performance in class A was 10.29 points higher than that in class B, and the average value of the student’s performance in class B was 10.29 points higher. The teaching experiment resulted in a mean score of 10.29 points higher for students in class A than for students in class B. 74.5% 7 of the students agreed with the “Trinity” foreign rule of law talent cultivation model. Using emerging information technology can foster the overall development of foreign rule-oflaw talents by integrating domestic and foreign legal resources.