The detection method of Raman spectroscopy relies on the chemometrics algorithms, and deep learning is the most popular are at present, which can be applied to the modeling of Raman spectroscopy. However, deep learning requires large samples for training, while Raman spectral collection is limited by equipment and labor cost. Obtaining large quantities of samples requires a higher cost, and also is suffered by fluorescence and other factors, which all restrict the application of deep learning to Raman spectral. In view of the above problems, the paper introduces the deep convolution generation counter network (DCGAN) to extract the characteristics of Raman peaks in the Raman spectrum, and generates a new Raman spectrum to expand the data set. At the same time, the reliability of DCGAN was proved by comparing with the slope-bias adjusting method, another method to expand the data set. In this paper, spectral selection criteria are designed and generated to fill the dataset with highly similar spectra, which is the first step for the application of deep learning in Raman spectra. In order to demonstrate that the generated spectrum has good comformality with the original spectrum, the paper sets up four groups of experiments for comparison: (1) the original Raman spectrum is input to SVM for classification, and the classification accuracy is 51.92%, (2) the original Raman spectrum was input to CNN for classification, and 75.00% classification accuracy was obtained, (3) the slope-bias adjusting method was used to generate the spectrum, which was input into CNN for classification, and the classification accuracy of 91.85% was obtained, (4) DCGAN was used to generate the spectrum, which was input into CNN for classification, and the classification accuracy was 98.52%. The comparison of the four groups of results proves the superiority of the Raman spectrum generated by DCGAN. The experimental results show that DCGAN can generated much alike spectrum through antagonism learning with only a small amount of Raman spectrum, and the generated spectrum is clearer than the original spectrum, reducing some interference factors, and has a preprocessing effect on the spectrum. Taking the advantage of DCGAN, a large number of high-quality data can be generated and filled into the original Raman spectral data set, and the sample size of the data set can be expanded, so that the deep learning model could be better trained, thus improving the accuracy of the classification or other model. This paper proposes a feasible scheme for applying deep learning method to Raman spectroscopy.