In order to realize the rapid,accurate and nondestructive identification of heavy mineral oil evidence in the field of forensic science,a multi-derivative spectral data combination analysis method based on spectral analysis technology was proposed in this paper.The spectral data for 80 kinds of heavy mineral oil samples of different models and manufacturers were collected by Fourier transform Raman spectral analysis method,and the classification models were constructed by combining the stoichiometry.In the constructed classification model of principal component analysis(PCA) combined with radial basis function neural network(RBF),the classifications of single original spectrum,first derivative spectrum and second derivative spectrum data were presented.The classification accuracies for the training set were 80.0%,86.7% and 86.2%,while those for the test set were 73.3%,80.0% and 72.7%,respectively.In the classifications of original spectrum-first derivative spectrum,original spectrum-second derivative spectrum and first derivative spectrum-second derivative spectrum after combination,the accuracies for the training set were 97.0%,96.7% and 100%,and those for the test set were 85.7%,90.0% and 100%,respectively.Results showed that the classification accuracy was higher when the derivative spectra and ordinary spectra were constructed.Among them,the classification model of PCA combined with RBF based on first derivative spectrum and second derivative spectrum data was the most ideal,with an accuracy of 100%.However,the model of K nearest neighbor algorithm had a low classification accuracy due to the influence of uneven samples.Construction of a classification model by the combination of derivative spectrum and the original spectral data could realize the fast,accurate and nondestructive identification of the heavy mineral oil samples,which could provide a certain reference for the application of spectral combination technology in forensic science and other analytical testing fields.