Diabetes is a disease of abnormal glucose metabolism that manifests as hyperglycemia. If the glucose level in the blood remains very low or very high for long periods, it could cause serious diseases including tissue damage, stroke, heart disease, blindness and kidney failure. According to the World Health Organization (WHO), currently there are around 450 million cases of diabetes in the world. With the increase in the number of diabetic patients, the demand for glucose measuring equipment has become increasingly urgent. As the currently popular invasive blood glucose measuring equipment will cause inconvenience and pain to patients and may even cause infections, it will inevitably bring psychological and physiological pressure to patients in the long term. Therefore, the realization of non-invasive blood glucose measurement has important clinical application value. Photoplethysmography(PPG) pulse wave contains abundant information about human cardiovascular physiology and pathology. This paper proposes a new method for non-invasive blood glucose detection based on spectral decomposition, aiming at the spectral information related to blood glucose concentration changes in the PPG signal that is difficult to observe in the time domain Continuous Wavelet Transform(CWT) is used to decompose the PPG signal from the corresponding scale and details in order to obtain the spectral component amplitude information related to the change of blood glucose concentration. Studies have found that there is a higher correlation between the change inthe amplitude of the PPG signal spectral components and the changes in the blood glucose concentration. Through the Oral Glucose Tolerance Test (OGTT), the detected blood glucose concentration and the obtained relevant PPG signal spectral components amplitude is modeled by partial least square regression, and the established model is evaluated. The Root Mean Square of Calibration (RMSEC) of the calibration set is 12.47 mg·dL^-1, which is 0.69 mmol·L^-1, and the Root Mean Square Error of Prediction (RMSEP) of the prediction set is 6.21 mg·dL^-1, which is 0.35 mmol·L^-1. The agreement between the predicted value of model’s blood glucose concentration and the reference value is 96.00%. The results of the OGTT experiments show that the spectral decomposition method can effectively separate the vibration characteristic absorption spectra of blood glucose molecular groups, and the blood glucose spectral component modeling can minimize the impact of physiological variability and various environmental conditions. The prediction results of the model meet the national testing standards (>95%). Clark grid error analysis show that the results predicted by this method can be used for daily blood glucose monitoring of patients.