The method of cloud model with entropy weight was adopted for the prediction of rock burst classification. Some main factors of rock burst including the uniaxial compressive strength (σc), the tensile strength (σt), the tangential stress (σθ), the rock brittleness coefficient (σc/σt), the stress coefficient (σθ/σc) and the elastic energy index (Wet) are chosen to establish evaluation index system. The entropy-cloud model and criterion are obtained through 209 sets of rock burst samples from underground rock projects. The sensitivity of indicators is analyzed and 209 sets of rock burst samples are discriminated by this model. The discriminant results of the entropy-cloud model are compared with those of Bayes, KNN and RF methods. The results show that the sensitivity order of those factors from high to low is σθ/σc, σθ, Wet, σc/σθ, σθ, σc, and the entropy-cloud model has higher accuracy than Bayes, K-Nearest Neighbor algorithm (KNN) and Random Forest (RF) methods.