Transgenic technology plays an important role in increasing crop yield and quality, and reducing pesticide use and production cost, but it also has a certain potential threat to the ecological environment. In order to prevent the abuse of genetically modified soybean in food, the research on rapid identification technology of genetically modified products is particularly urgent. UV Raman spectroscopy detection technology can be effectively used in material telemetry and identification with many advantages, such as long-distance non-destructive telemetry detection, simplicity, efficiency, rapidity and accuracy. Based on UV Raman spectroscopy, the feasibility of identifying transgenic/non-transgenic soybean oil and other types of edible oil was studied. The UV Raman spectra of five different edible oils (500 samples of each brand of GM/non-GM soybean oil and 100 samples of one kind of rice oil, 2 100 samples in total) in the wavelength range of 3 500~400 cm-1(268~293 nm) were collected. In order to improve the signal-to-noise ratio of spectral data and ensure the accuracy of classification, we used Savitzky-Golay filtering to denoise, adaptive iterative weighted penalty least squares (airPLS) to correct baseline and multiple scattering correction (MSC) to standardize spectrum. According to the UV Raman fingerprint of soybean oil, the main chemical components were analyzed, including fats, proteins and amides. We divided each kind of soybean oil into the training set and test set according to 1∶1, input the training set data into a support vector machine (SVM) for training, and established the best model by 10-fold cross-validation. The recognition accuracy was 99.81%, which had a significant effect on detecting the transgenic soybean. Principal component analysis (PCA) is used for data dimensionality reduction, and 8 principal components were extracted, with a cumulative contribution rate of 74.84%, which can represent most of the characteristics of the original data. On this basis, the preprocessed spectral data were divided into the training set and test set according to 4∶1. The partial least squares regression discriminant analysis (PLS-DA) and 10-fold cross validation method were used to establish the best PLS-DA model of the whole spectrum (the discrimination threshold was set to 0.5) with the accuracy of 70.95%. It is shown that UV Raman spectroscopy can accurately and rapidly identify GM/non-GM soybean oil and rice oil. The study provides an important practical and theoretical basis for the on-site detection of transgenic soybean oil and its food and is of great significance in promoting the development of telemetry identification technology for transgenic products.