In this study, the Raman spectra of three brands of wood lacquer samples, such as Chenyang brand, are examined to realize the rapid nondestructive detection as well as the accurate identification and classification of wood lacquer. Subsequently, the processing effects of different preprocessing methods, such as the baseline correction, Savitzky-Golay nine-point smoothing, first derivative, and second derivative, are investigated. Three models of characteristic-band ratio, Fisher discriminant, and K nearest neighbor (KNN) are also established. The experimental results indicate that the characteristic-band ratio method is capable of characterizing the features of the three wood lacquer samples with 1358 cm ^-1/1239 cm ^-1; further, the Raman spectral model combined with Fisher discriminant based preprocessing methods of baseline correction, smoothing, and second derivative exhibits an optimal classification accuracy of 100%. However, the accuracy of the KNN discriminant model is limited to 88.5% under the same preprocessing. Therefore, the second-derivative based Raman spectroscopy combined with the characteristic-band-Fisher-KNN method can provide a new rapid nondestructive analysis method for the accurate detection of different brands of wood lacquer, exhibiting universality and certain reference significance.