Near Infrared spectroscopy (NIRS) has been widely used in the discrimination (classification) of pharmaceutical drugs. In real applications, however, the class imbalance of the drug samples, i.e., the number of one drug sample may be much larger than the number of the other drugs, deceases drastically the discrimination performance of the classification models. To address this class imbalance problem, a new computational method — the scaled convex hull (SCH)-based maximum margin classifier is proposed in this paper. By a suitable selection of the reduction factor of the SCHs generated by the two classes of drug samples, respectively, the maximal margin classifier between SCHs can be constructed which can obtain good classification performance. With an optimization of the parameters involved in the modeling by Cuckoo Search, a satisfied model is achieved for the classification of the drug. The experiments on spectra samples produced by a pharmaceutical company show that the proposed method is more effective and robust than the existing ones.