A new method was put forward to study vinegar brand traceability based onthree-dimensional fluorescence spectra technology combined with quaternion principal component analysis. Firstly, the three-dimensional fluorescence spectral data of vinegar samples with different brands were acquired by F7000 fluorescence spectrometer. The contour and 3D fluorescence spectra about four different brands vinegar were acquired and the three-dimensional fluorescence contour maps were analyzed; Then the parallel quaternion matrix representation model of vinegar three-dimensional fluorescence spectral data was established by using the emission spectral data under excitation wavelength of 380, 360 and 400 nm respectively. The quaternion features were extracted using quaternion principal component analysis, and the exacted quaternion principal components were conducted feature fusion based on operations of multiplication, modulus and summation respectively; At last, the fusion features were as the input of K-Nearest Neighbors, and the optimal classification model of vinegar brand traceability was made. The relationships between the model classification accuracy and the three different feature fusion methods and the number of quaternion principal components were discussed. According to the analysis results with 120 vinegar samples of four different brands, the fusion feature obtained by summation operation can establish the best traceability model by using the least number of features, and the accuracy of the prediction set can reach 100%. The results of this study showed that the quaternion principal component feature extraction and feature fusion methods can represent the rich information contained in the three-dimensional fluorescence spectral data in parallel, which provides a new idea for the analysis of three -dimensional fluorescence spectral data.