The qualitative identification of Raman spectroscopy has been widely used in many industries and research fields, but the preprocessing in traditional Raman spectroscopy analysis mainly relies on human experience. Although spectral feature extraction can reduce the signal dimension, it also causes partial spectral information loss. Materials with similar characteristics have high spectral similarity. In addition, due to the interference of measurement environment and the various errors in the analysis process, the final classification accuracy is not ideal enough. Aiming at these problems, this paper proposes a novel Raman spectral qualitative classification method based on one-dimensional convolution neural network. The Raman spectra of three different estrogen powders, estrone, estradiol and estriol, were collected, and three Raman spectral data augmentation methods were designed to construct Raman spectral database; a one-dimensional convolution neural network classification model for Raman spectral data was proposed, which integrated the whole process of spectral preprocessing, feature extraction and qualitative classification. The hyper-parameters and training process of the proposed classification model were optimized and the accuracy was tested by simulation experiments. Experimental results indicated that the 1D-CNN model can classify three similar estrogen powders Raman spectroscopy with the highest classification accuracy of 98.26%. No spectral preprocessing and feature extraction steps were required in the analysis process, which simplifies the spectral signal analysis process and can retain more vital information. In addition, when the noise intensity of the simulated measurement reached 60 dBW, the classification accuracy of the traditional methods decreased obviously in varying degrees, but the 1D-CNN model could still achieve 96.81% accuracy. Compared with traditional Raman spectral classification methods, the proposed method was less affected by the noise of measurement process and had stronger robustness, which was suitable for Raman spectral signals with strong noise measured in more complex environments. The results of this study show that deep learning method has great application potential in the field of analysis of Raman spectroscopy.