Research on Multidimensional Data Analysis and Predictive Modeling for International Political Stability Assessment

International political stability has been a hot issue of global concern. Starting from the factors influencing international political stability, the study constructs a prediction model based on the CNN neural network and takes some international countries as the case study objects to predict the political stability of each country. The system of political stability influencing factor indicators is constructed. 13 influencing factor indicators are selected and divided into three categories, and the warning intervals of early warning indicators are determined. The study trained and validated the CNN neural network model, and the results showed that the mean square error of the prediction model was 1.862 × 10⁸, the average accuracy of the model was 1.53%, and the relative error of each year was within ±4%, which reached the set model accuracy, and thus the prediction model proposed in this paper can be highly accurate. Subsequently, 44 countries along the route were selected to carry out political stability prediction research using the early warning model, and finally, the political stability of some countries was investigated according to the stability interval level classification method, and the results showed that the political stability of Syria, Iran, and many other countries had a high-risk phenomenon.