For the purpose of the authentication of sorts as well as the prediction of contents of the oils which were adulterated into olive oil, 117 olive oil samples adulterated with sunflower seed oil, soybean oil and corn oil were detected by Raman spectroscopy, and least squares support vector machine (LS-SVM) based on multiple iterative optimization was used to identify the type of the adulterant oil, and the composite recognition rate was 97%. In addition, methods such as LS-SVM, ANNs and PLSR were used to build the Raman spectra calibration model of the adulterant oil (sunflower seed oil, soybean oil and corn oil) contents respectively, the results indicated that LS-SVM had the best predictive performance, and the root mean square error of prediction (RMSEP) ranged from 0.007 4 to 0.014 2. Research results showed the method based on Raman spectroscopy and LS-SVM was accurate, fast, simple and non-destructive for adulterated olive oil detection.