Quickly and effectively identifying the water contaminants is vital for reducing the impact of sudden drinking water pollution incidents. PCA is mostly used to extract the feature of different contaminants in drinking water with UV-Vis spectra. However, for the organic contaminants with high similarity in UV-Vis spectra, the identification result is ineffective when only extracting the feature of the largest variance direction from the data-driven point of view. This paper studies the classification of organic contaminants in water distribution systems developed by SPA and multi-classification SVM using UV-Vis spectroscopy. Firstly, the original spectral data of phenol, hydroquinone, resorcinol and m-phenylenediamine are measured by UV spectrometer and pretreated. The correlation between wavelength and concentration of four contaminants was compared. The peaks between phenol and resorcinol, hydroquinone and m-phenylenediamine are overlapped seriously, the classification results can interfere easily. In feature extraction, the SPA is introduced to select the organic contaminants’ characteristic wavelengths of UV-Vis spectra. Then, multiple linear regression analysis is carried out to choose the optimal parameter combination, which corresponds to the minimum prediction standard deviation. Based on this, the multi-classification support vector machine is used to form an identification model for drinking water organic contaminants. Finally, the classification results of spectral data based on full spectrum, PCA and SPA under different classification methods and different concentrations are compared and analyzed, and the applicability and stability of SPA are further explained. Experimental results demonstrate that SPA-based feature extraction method eliminates the interference of multi-collinearity and amplifies the difference among the UV-Vis spectra of different organic contaminants, thereby improving the accuracy of the classification model. This method has certain reference value for solving the problem of identifying the types of pollutants with overlapped peaks in the drinking water.