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This invention belongs to the interdisciplinary field of biomedical engineering and artificial intelligence-assisted diagnosis. It discloses a training method for a SERS spectral classification prediction model and its application. The disclosed SERS spectral classification prediction model training method combines CatBoost feature selection and deep learning. First, surface-enhanced Raman spectroscopy (SERS) data of serum from different categories of subjects is collected. After baseline removal, filtering, and normalization preprocessing, CatBoost gradient boosting algorithm is used to evaluate feature importance, considering the high-dimensional redundancy of the spectral data, and to select a subset of discrete feature bands containing key biomarker information. This feature subset is then input into a one-dimensional convolutional neural network model for training. The model constructed by this invention possesses deep feature mining capabilities, and the biological interpretability of the decisions is verified through SHAP analysis. It effectively solves the problems of large spectral noise interference and difficulty in extracting weak pathological features in traditional methods. For example, it can be used for the auxiliary diagnosis of coronary heart disease, achieving non-invasive, rapid, and high-precision classification and diagnosis of coronary heart disease and its subtypes.