Abstract: Disclosed are a method and a system for forecasting an agricultural product price based on signal decomposition and deep learning. The method includes: S1, obtaining price subsequences by performing a complementary ensemble empirical mode decomposition (CEEMD) on an original price sequence of agricultural products; S2, obtaining a reconstructed sequence based on the price subsequences; S3, obtaining data features of the reconstructed sequence based on the reconstructed sequence; and S4, constructing a Bi-directional Sequence to Sequence (BiSeq2seq) model, and inputting the data features of the reconstructed sequence into a CCS-Bi-directional Sequence to Sequence (CCS-BiSeq2seq) model to obtain a forecasting result.