The quality of green tea changes rapidly due to the oxidation and degradation of polyphenols during storage. Herein, a simple and fast Surface-enhanced Raman spectroscopy (SERS) strategy was established to predict changes in green tea during storage. Raman spectra of green tea with different storage times (2020–2015) were acquired by SERS with silver nanoparticles. The PCA-SVM model was established based on SERS to quickly predict the storage time of green tea, and the accuracy of the prediction set was 97.22%. The Raman peak at 730 cm−1caused by myricetin was identified as a characteristic peak, which increased with prolonged storage time and exhibited a linear positive correlation with myricetin concentration. Therefore, SERS provides a convenient method for identifying the concentration of myricetin in green tea, and myricetin can function as an indicator to predict the storage time of green tea.