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The characteristics of the spectral data are essential for the qualitative analysis of substances. Traditional classification models often need to preprocess the data. However, misuse of preprocessing may change the characteristic information carried by the original data which result in poor model performance. This paper proposes an end‐to‐end deep learning method that combines residual modules to learn features from raw data to improve model performance, which called ResidualSpectra. ResidualSpectra model is compared with three convolutional neural network (CNN) models on the original data. The 15 preprocessing approaches are used to evaluate the preprocessing impact by testing five open‐access mid‐infrared, near‐infrared, and Raman spectra datasets (fruits, meats, olive_oils, Tablets_Nir, Tablets_Raman). In most cases, the ResidualSpectra model performs better than the other three CNN models on five datasets and obtains better results in original data than in preprocessed data. The model is compared with linear discriminant analysis (LDA), nonlinear artificial neural network (ANN), support vector machines (SVM) for original and preprocessed data. The results show that the ResidualSpectra method provides improved results over traditional classification methods in most scenarios. Residual Spectra model is developed to learn features from the original spectra without pre‐processing. This study detailed a deep learning method for qualitative analysis of vibrational spectra. The model includes convolutional layers combined with residual structure to learn features from spectra data. [ABSTRACT FROM AUTHOR]