Abstract: Waterborne wood coating is a kind of trace evidence commonly found in crime scenes, and it is widely concerned in the forensic science field. In order to detect and classify the complex chemical components in waterborne wood paints, Raman spectrum, which has high resolving power and non-destructive testing characteristics, were used in this study. Combined with two data mining techniques of principal component analysis and radial basis function neural network, the Raman spectra of 38 waterborne wood lacquer samples from 3 brands were analyzed. The results show that the classification accuracy of 78.9% is obtained under the radial basis function model. Fourier Raman spectroscopy combined with radial basis function model was used to identify and classify waterborne wood coating, which provided new ideas for the classification of wood lacquers in practice.