To investigate the differences of spectral features among typical features and to address the complicated preprocessing and low accuracy of traditional spectral classification methods, this study considers four features: soybean, corn, rice, and bare soil, as examples, comprehensively investigates the importance of variables in classification, and conducts a comparative analysis and validation of deep learning and traditional methods. First, we use the continuous projection algorithm (SPA) for the baseband screening and compare and analyze the classification accuracy of two deep learning models: the one-dimensional convolutional neural network (1DCNN) and the long short-term memory artificial neural network (LSTM), under the conditions of the original spectrum, the feature band, and the partial feature band, to evaluate the information-carrying capacity of the feature band to the original spectrum. Then, for the misclassification problem, we use the progressive band-screening method to train the misclassified samples again with a combination of basic variables until the classification accuracy does not increase significantly, and analyze the spectral characteristics and misclassification behavior of the misclassified samples. Finally, we compare the classification accuracy of different methods. The results show that the basic band screening can eliminate a large amount of redundant information in the spectral data, simplify the network structure, and improve the model efficiency. The advanced band-screening method can incrementally add effective spectral information for misclassified samples, which helps improve the classification accuracy of traditional methods. The deep learning method can also achieve high classification accuracy without preprocessing steps such as spectral transformation, which is significantly better than the traditional method. However, its training process is more complicated and less interpretable than that of the traditional method.