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Raman spectroscopy is a popular technology for material identification, but it encounters difficulties when distinguishing components with closely related substances by intuition and experience. The distinction of fats and oils is a typical example, which is significant in the food industry. In this work, the Raman spectroscopy and deep learning algorithm are combined to analyze closely related animal fats (lard, butter, mutton fat and chicken fat) and vegetable oils (soybean oil and peanut oil) in a dataset. A deep neural network founded on the VGG architecture with attention mechanism is developed, reaching an accuracy of 100% for fats and oils classification. By combining Raman spectroscopy with deep learning, this research provides a potent technique for tackling the identification of similar substances. In this work, the Raman spectroscopy and deep learning algorithm are combined to analyze closely related animal fats (lard fat, butter fat, mutton fat and chicken fat) and vegetable oils (soybean oil and peanut oil) in a dataset. A deep neural network founded on the VGG architecture with attention mechanism is developed, reaching an accuracy of 100% for fats and oils classification. [ABSTRACT FROM AUTHOR]