Aiming at the difficulty of finding terrorist attack organizations and the imbalance of terrorist attack data samples, a terrorist attack organization prediction method based on feature selection and hyperparameter optimization was proposed. First, by taking the advantage of Random Forest (RF) in dealing with imbalanced data, the backward feature selection was carried out through the RF iteration. Second, four mainstream classifiers including Decision Tree (DT), RF, Bagging and XGBoost were used to classify and predict terrorist attack organizations, and the Bayesian optimization method was used to optimize the hyperparameters of these classifiers. Finally, the Global Terrorism Database (GTD) was used to evaluate the classification prediction performance of these classifiers on the majority class samples and minority class samples. Experimental results show that the proposed method improves the classification and prediction performance of terrorist attack organizations, and the classification and prediction performance is the best when using RF and Bagging, with the accuracy of 0.823 9 and 0.831 6 respectively. Especially for minority class samples, the classification and prediction performance when using RF and Bagging is significantly improved.