Taking 116 honey samples as the research object, this study aims to rapidly detect the physicochemical properties of honey using infrared spectroscopy. The physicochemical properties of honey samples were determined, including the carbohydrate (fructose, glucose, sucrose, maltose, and lactose), moisture content, titration acidity, pH, electrical conductivity, viscosity, amylase value, HMF, proline, and color (L*a*b*). Principal component analysis (PCA) was implemented to explore the correlation among various physicochemical indexes of honey. Near Infrared spectrum (NIR) and mid-infrared spectrum (MIR) were used to obtain the three-dimensional information of "compound, aroma and taste" of honey samples. The Partial least squares (PLS) models were established using physicochemical indexes after NIR and MIR. Correlation coefficient (R²) and predictive root mean square error (RMSEP) were used to evaluate the quantitative accuracy of NIR and MIR indexes. The quantitative analysis of physicochemical properties was also implemented to compare the quality evaluation. Proline and amylase values were taken as the indicators, in order to clarify the effects of spectral technology fusion on the quantitative analysis at the level of information source (data) fusion. The results showed that there was a positive correlation between the color (L*a*b*), electrical conductivity, and pH, indicating the high mineral content with the deeper color and the higher conductivity value. A negative correlation between pH and titration acidity, namely the pH was low, while the titration acidity was high in honey. There was also a negative correlation between the water content and viscosity. The lower the water content of honey was, the higher the viscosity was. some indexes of honey were interrelated as well. The NIR and MIR models were established with the excellent ability of quantitative analysis for the fructose, glucose, reducing sugar, fructose/glucose, water content, viscosity, pH, and color (L*a*b*) (R²>0.9). The R² and SEP of the model were acceptable for the electrical conductivity, sucrose, maltose, and titration acidity, indicating better precision in the rapid quantitative analysis. There was a low quantitative accuracy of amylase value content, proline, and HMF. There was a low accuracy of the improved model for the amylase value, proline, and HMF after the rapid quantitative analysis. The indexed with the higher prediction accuracy of NIR were ranked as fructose(R²=0.908), sucrose(R²=0.906), water content(R²=0.975), electrical conductivity(R²=0.935), viscosity(R²=0.949), and pH(R²=0.947). The indexes with the higher accuracy of MIR prediction were the glucose(R²=0.813), maltose(R²=0.798), reducing sugar(R²=0.711), fructose/glucose(R²=0.942), titration acidity(R²=0.890), amylase value(R²=0.641) and Color, L*a*b*. The R_c of the proline quantitative model increased from 0.614 to 0.825, whereas the RMSEP decreased from 54.95 to 49.57 mL/(g·h) after the fusion of NIR and MIR in the data layer. The quantitative model of amylase value was not optimized during this time. From the perspective of instruments, accessories, and spectral acquisition, the ATR accessories used for the MIR spectral acquisition were large and expensive, particularly only limited to laboratory use. The NIR can be expected for the practical application of rapid detection of honey quality under comprehensive conditions. Therefore, it is feasible for the NIR and MIR to rapidly quantify some physical and chemical indexes of honey. Data fusion can posed a positive impact on the prediction model in the implementation process.