In order to improve the accuracy of the whole blood hemoglobin (Hb) concentration prediction model, the original whole blood transmission spectrum signals were first preprocessed by using centering, auto scaling, standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky-Golay (SG) smoothing combined with MSC. And the best preprocessing effect was obtained with a R2 value of 0.9441 by using SG smoothing combined with MSC. The width of the SG smoothing window was discussed, and the optimal width is 27.The baseline shift of the whole blood absorbance signals was eliminated, and the signal-to-noise ratio was improved after data preprocessing. The 190 samples were divided into a calibration set (corresponding Hb concentrations from 10.6 to 17.3 g·dL^-1) of 143 samples and a validation set (corresponding Hb concentrations from 10.3 to 17.3 g·dL^-1) of 47 samples. The model's applicability was ensured when two sets have a similar distribution and range of Hb concentrations. And then, the Monte Carlo uninformative variable elimination (MC-UVE) was used to select the informative wavelength, which simplified the model structure and increased the proportion of useful wavelengths. When the Monte Carlo iteration number was 1000, 191 wavelength points were selected from the 700 wavelengths of the whole blood absorbance spectrum to build the whole blood Hb concentration partial least squares (PLS) model. Finally, a comparison was performed among the model based on the original whole blood transmission spectrum, the model based on the whole blood absorbance spectrum, the SG-MSC-PLS model, the SG-MSC-MC-UVE-PLS model and an existing model. In addition to this, the number of selected wavelengths based on MC-UVE was much smaller than the total number, but the predictive effect was much better, which was beneficial to improve the calculation efficiency of the model. The results indicate that the SG-MSC-MC-UVE-PLS method effectively increases the signal-to-noise ratio of the whole blood absorption spectrum signal and simplifies the model. Besides, our procedure's prediction accuracy and calculation efficiency of the model was improved by our procedure, which has reference significance for the development of hemoglobin concentration detection technology.