The classification of stars has always been a hot topic in recent astronomical research. The classification of stellar subtypes is significant for exploring stellar evolution and identifying rare celestial bodies. This paper designs the SSTransformer (Stellar Spectrum Transformer) classification model for the LAMOST spectral subtype classification problem. The model mainly comprises three parts, including the input module, the embedding module, and the SST encoding module. In the input module, the spectral data is processed into blocks, which are mapped to vectors through a linear projection layer. In the embedding module, in order to extract useful data features, the output of the linear projection layer is added to a learnable category embedding block. In order to preserve the position information, a position embedding block is added, and then these data feature vectors are sent to the SST encoding module. Finally, the data features are extracted in the SST coding module, and the stellar spectrum is classified using the multilayer perceptron combined with the new features. In this paper,the spectral data of type A, F, G, K, and M starsis all from the one-dimensional low-resolution spectra in LAMOST DR8, 35256 pieces of one-dimensional spectral data are used for training the SSTransformer model, and 8 815 pieces of one-dimensional spectral data are used for testing the SSTransformer model. In order to speed up the convergence of the model, Z-Score normalization is used for the data. Because this is a multi-classification problem, the experiment adopts five evaluation indicators: accuracy rate, precision rate, recall rate, F1-Score, and Kappa coefficient. The experimental results show that the SSTransformer model can effectively screen and classify one-dimensional stellar spectral data, and the classification accuracy reaches 98.36%, which is higher than the support vector machine (SVM) algorithm, eXtreme Gradient Boosting (XGBoost) algorithm, and convolutional neural networks (CNN).