Anthocyanin is a natural water-soluble flavonoid pigment with various medicinal values, which is widely found in mulberry and has become an important indicator for evaluating the quality of mulberry products. Because the implementation of the traditional detection methods could cost a lot of time and effort, it is significant to achieve the rapid detection of anthocyanin content in the development and utilization of mulberry products. In this study, anthocyanin in mulberry was taken as the research object to explore the relationship between anthocyanin and Raman spectral characteristics and the feasibility of quantitative detection of anthocyanin by Raman spectroscopy. The Raman spectra of mulberry and three kinds of anthocyanin were analyzed. The peak positions at 545, 634 and 737 cm^-1 could be regarded as Raman characteristic peaks of anthocyanin in mulberry, to judge whether there was anthocyanin in mulberry, and the content of anthocyanin could be qualitatively determined as per the peak values. The spectroscopic data were preprocessed with the multiplicative scatter correction (MSC), baseline correction (airPLS), Normalized and the combined methods, and the best preprocessing method was selected by combining PLSR. It could be found that the best preprocessing method was airPLS+MSC+Normalized, and the PLSR model had a better effect. In the modeling set, the coefficient of determination is 0.97 and RMSE_c is 2.74, while in the prediction set, the coefficient of determination is 0.82, and RMSE_p is 13.69. Based on the spectra preprocessed with airPLS+MSC+Normalized, competitive adaptive reweighting sampling (CARS) was adopted to extract the characteristic wavelengths of the spectra. PLSR model and SVR model were established respectively regarding the selected wavelength variables as input variables, and the research into the predicting effects of both models was conducted. As per the results, the two models processed with CARS could predict the content of anthocyanin accurately, and the SVR model established with the screening of CARS variables had the best performance in the prediction accuracy, with the coefficient of the determination being 0.98 and RMSE_c being 1.92 in the modeling set, and the coefficient of the determination being 0.94 and RMSE_p being 4.70 in the prediction set. Therefore, the rapid and accurate prediction of anthocyanin content in mulberry could be achieved by Raman spectroscopy.