Identifying celestial spectra is essential for making new astronomical discoveries and conducting detailed studies of celestial objects. The LAMOST DR8 v1.0 release of low-resolution spectral data contains approximately 530 000 spectra named “Unknown”. The reason is that they have no category labels. And 88.56% of these spectra have signal-to-noise ratios between 0 and 10. Therefore,the effective output of LAMOST will increase if we analyze these spectra. In this paper,we propose an ODS-YOLOv7 model to deal with the problem of the “Unknown” spectral classification. It is an end-to-end category prediction model and is suitable for one-dimensional spectra. We also add a one-dimensional convolutional attention module to improve the accuracy of spectra recognition. After training on a set of known category spectra with signal-to-noise ratios between 0 and 10, the ODS-YOLOv7 model can learn the effective features of the low signal-to-noise spectra. Thus,it can enable us to predict “Unknown” spectra. Experiments show that the model has an F1-score of 0.98,0.95,and 0.95 for the spectral identification of low signal-to-noise stars,galaxies,and quasars spectra with known labels. In the meantime,ODS-YOLOv7 obtains the best results in comparison experiments with traditional algorithms KNN,RF,DT,SVM,and deep learning algorithms 1D CNN,1DSSCNN,ResNet,DenseNet,and VIT. The experimental results also give confidence in the predictions of the ODS-YOLOv7 model for the “Unknown” spectra in DR8 v1.0,with 92% of the confidence levels above 60%. To ensure the quality of the model output,only spectral categories with a prediction confidence level greater than 99% are selected as output in this paper. Ultimately,37.19% and 47.03% of the “Unknown” spectra released in DR8 v1.0 and DR9 v0,respectively,are predicted by this model.In addition,the paper tests the accuracy of the models predictions using manual authentication. To improve the interpretability of the model,the paper takes the Grad-CAM method for two-dimensional image visualisation. It improves it into an algorithm suitable for visualising one-dimensional spectral data to predict output features. Experiments show that the model focuses on different features in the visualisation of different classes of astronomical features and that the model is good at predicting low signal-to-noise “unknown” spectral classes.