As the generative model needs modelling complex joint probability density and evaluating many parameters, a discriminant semi-supervised classification algorithm based on the regularization of minimum entropy is proposed. This algorithm uses Havrda-Charvat＇s structural α-entropy as the regularization item of the objective and employs the quasi-Newton method to solve the objective, which makes the algorithm discriminative and inductive and reduces the dependence of the algorithm on the model. At the same time, the algorithm can predict the labels of the out-of-sample data points easily. Simulated results of several UCI datasets demonstrate that the proposed algorithm is of low classification error even with few labeled data.