In this paper, a method for discrimination of different bands liquor with strong aroma type based on three-dimensional fluorescence spectrum technology was developed. Firstly, the three-dimensional fluorescence spectra of seven different brands liquor were measured by the FLS920 fluorescence spectrometer which produced by Edinburgh in England. The spectral shows that different bands liquors have similar fluorescence characteristics and it’s difficult to distinguish them only with Fluorescent characteristic parameters. Because of this, the first-order and second-order partial derivatives respect to fluorescence emission wavelength on each of the excitation wavelength were carried out in this paper. Daubechies-7 (db7) orthonormal wavelet with compact support was used to compress the spectral data. The forth approximate coefficients were finally chosen as the new data matrix. Then the new data matrix was analyzed by principal component analysis (PCA) and the principal components were extracted to be used as the inputs of support vector machine (SVM). The K-fold cross validation was applied to optimize the parameters c and γ and the prediction model was constructed in the end. Fourteen samples were selected randomly from each brand that in total of ninety-eight samples were selected as the training set, and the rest forty-two samples were collected as the prediction set. The effect of three different spectral data after processing on the model is compared: original data, the first-order and second-order partial derivatives on the spectral data. The results show that the three-dimensional fluorescence spectra with the pretreatment of second-order partial derivatives coupled with PCA and SVM can make a good performance on the brands identification of strong aroma type liquors, the accuracy of the established model and prediction accuracy were 98.98% and 100%, respectively. This method has the advantage of easy operation, high speed, low cost and provides a good help in the detection and identification of Chinese liquor.