Abstract: Taking Awati Musalaisi as research object,E-nose and SPME-GC-MS technologies for different raw wines(G1,G2,G3)and Musalaisi(G4,G5,G6,G7)to detect the volatile components. The results showed that the PCA model had a discriminant index(DI)of more than 80,which indicated that the sample could be well distinguished. The DFA model was used to predict the unknown samples,combined with PCA model and DFA model could effectively distinguish the different types of Musalais. A total of 164 volatile compounds were detected by GC-MS.Among them,G1(67species),G2(47species),G3(55species),G4(45species),G5(54species),G6(57species)and G7(51species).It could be seen from PCA analysis that the alcohols,esters and acids were the main aroma substances in Musalaisi. Among that,phenethyl alcohol,1-amylalcohol,caproic acid,caprylicacid,lauric acid,sebacic acid,ethyl acetate,ethyl phenylacetate,octanoic acid-ethyl ester and palmitic acid ethyl ester were the most important.The determinative coefficient of PLS model established by GC-MS and electronic nose data was more than 90.0%,and had a good correlation.