In this paper, a new method for pharmaceutical discrimination by the near infrared spectrum is proposed, which is based on the sparse denoising autoencoder (SDAE) combined with Gauss process (GP). First of all, the Mexican hat wavelet transform was used to eliminate noise and baseline drift from the spectra data, then SDAE network was used to extract the feature and reduce dimension of spectrum. Finally, GP was used for binary classification, in which the GP selected the spectral mixture (SM) kernel function as its covariance function. This classification method was named as wSDAGSM. Autoencoder network has a strong ability of model representation, and GP classifier has the advantage in dealing with small sample data. The WSDAGSM network is able to obtain fewer dimensions and more valuable features by SDAE learning to represent the input data. Meanwhile, the spectral mixture kernel function which has a good expression was used as the covariance function of the GP in the WSDAGSM network. Therefore the WSDAGSM network is conducive to more accurate classification of spectral data. With near infrared spectra of Erythromycin Ethylsuccinate and other pharmaceuticals as experimental data, some classification methods were used after Mexican hat wavelet transform, they were BP neural network (wBP), support vector machine (wSVM), SDAE combined with binary classification of Logistic (wSDAL), SDAE combined with binary classification of GP selected the squared exponential (SE) kernel function (wSDAGSE). And another method was also applied, which was SDAGSM network without Mexican hat wavelet transform. All above methods were used for comparing with wSDAGSM network. Experimental results show that SDAGSM can effectively improve the classification accuracy and stability by applying the wavelet transform to the spectral data. The proposed method wSDAGSM is superior to other classifiers in terms of classification accuracy and stability of the classification results.