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The application discloses a propolis component intelligent identification and tracing system and method, relates to the technical field of computer vision and pattern recognition, and comprises the following steps: a data acquisition module is used for collecting spectral data, chromatographic data and traceability information such as origin and processing; a data preprocessing unit is used for filtering, baseline correction and denoising, eliminating overlapping peaks, normalizing data and synchronizing time stamps; a component feature extraction module is used for screening 10 spectral characteristic peaks, calculating chromatographic characteristics, and reducing the dimensionality to 32 dimensions after splicing through principal component analysis; an intelligent identification module is used for determining purity grades and confidence levels by using a CNN-LSTM model; a traceability management module is used for storing full-link information in a block chain and managing and querying permissions in a distributed database; and a result output module is used for visually displaying results and triggering an alarm when the confidence level or the traceability integrity is not up to standard. The application improves identification accuracy through multi-source fusion, the model can be updated online to adapt to new scenarios, and the quality of propolis and the market order are efficiently guaranteed.