Traditional one-dimensional spectra perform poorly when classifying celestial objects with a low signal-to-noise ratio (SNR). Therefore,the paper uses two-dimensional spectra and proposes a feature fusion model called TDSC-Net (Two-Dimensional Spectra Classification Network),incorporating an attention mechanism for stellar classification. TDSC-Net employs identical feature extraction layers to get features from the two-dimensional spectra of stars,specifically from the blue and red ends. The extracted features are fused and employed for the classification task. The stellar spectral data in this experiment is selected from the LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope) database. They are using Z-score normalization on the spectra to reduce convergence difficulties caused by significant variations in spectral flux values and evaluating the model performance by four metrics:Precision,Recall,F1-score,and Accuracy. The experiments consist of two parts:In the first part,TDSC-Net is employed to classify A,F,G,K,and M-type stars to validate the reliability of using two-dimensional spectra for multi-class stellar classification. In the second part,the two-dimensional spectra are classified based on different SNRs to investigate the impact of SNRs on classification accuracy. The first parts results show that the five-class classification accuracy reaches 84.3%. The classification accuracies of A,F,G,K,and M types are 87.0%,84.6%,81.2%,87.4%,and 89.7%,respectively. These accuracies are higher than the results obtained from one-dimensional spectra classification after spectra extraction. The results of the second part indicate that even in the low SNR (SNR<30),the accuracy of two-dimensional spectra classification can still reach 78.9%. Once the SNR surpasses 30,the impact of SNR on spectra classification becomes less significant. These provide evidence for the importance of using two-dimensional spectra classification in low SNR and demonstrate the effectiveness of TDSC-Net in stellar spectra classification.