The traditional method for determining the content of Eburicoic acid is HPLC, which is inefficient and cumbersome to operate. To achieve rapid and non-destructive monitoring of Eburicoic acid, this paper attempted to establish a partial least squares (PLS) regression model based on near-infrared spectroscopy (NIR) to predict the Eburicoic acid content in Fomes officinalis Ames decoction pieces. Firstly, the traditional HPLC method was used to test the content of Eburicoic acid in Fomes officinalis Ames, and the test results were used as indicator values. Secondly, near-infrared data was collected, and five spectral transformation methods were used to preprocess spectral data, namely Multiplicative Scattering Correction (MSC), Normalized Normal Variation (SNV), Savitzky Golay Smoothing (7 points), First Derivative Transformation (FD), and Second Derivative Transformation (SD). Finally, wavelength selection was performed through competitive adaptive reweighted sampling (CARS) and the PLS model was optimized, greatly reducing the number of spectral variables and significantly improving the performance of the PLS model, especially the SNV-CARS-PLS model, which only accounted for 5.53% of the total spectral wavelength. The R² value for prediction sets 0.982 3. The root mean square error (RMSEP) value for prediction is 0.103 7%, and the residual prediction deviation (RPD) value is 5.34. The t-tests indicated no significant difference in precision and accuracy between the results of the optimal model and that of the traditional HPLC method. The research results indicate that it is feasible to establish PLS models based on near-infrared spectroscopy combined with a competitive adaptive reweighting algorithm for non-destructive detection of Eburicoic acid content in Fomes officinalis Ames decoction pieces.