Soil phosphorus is one of the most important nutrients for plants. Phosphorus is highly dynamic in soil, and it is not easy to detect it. It has no obvious absorption band in the visible-near infrared range. Therefore, rapid phosphorus detection methods based on other spectral methods are of great significance for developing precision agriculture and/or smart agriculture. Raman spectroscopy has the characteristics of interference-free from water, less sample pretreatment, and complementary to infrared spectral information. Many researchers have tried to use Raman spectroscopy to detect soil phosphorus. However, the weak Raman signal and poor stability restrict its application in soil sensing. To clarify the quantitative relationship between Raman spectra and phosphorus, water-soluble phosphorus (KH2PO4) was used as a research target, and the effects of KH2PO4 solutions with different phosphorus concentrations on Raman spectra were studied. The raw spectra (RS) were processed by moving average (MA), MA+baseline correction (BL), MA+standard normal variable (SNV), and MA+multivariate scattering correction (MSC). Low concentrations (0.02~5 g·L-1) and high concentrations (5.21~93.87 g·L-1) of KH2PO4 and their relationships with Raman spectrum variation characteristics were analyzed. A prediction model for phosphorus concentration content was established. The results show that: (1) the coefficient of variation of the spectra in the low concentration range and the high concentration range were significantly different, and the dispersion degree of the spectra in the high concentration range was larger; (2) No obvious Raman peaks were detected in the Raman spectra of the low concentration range. Concentration changes exhibited significant baseline shifts. The coefficient of Determination (R2) of partial least squares regression (PLSR) models was 0.28~0.36; (3) Characteristic Raman peaks at 863 and 1 070 cm-1 were identified in the high concentration range, and PLSR modeling results were R2=0.65~0.7. The MA+SNV and MA+MSC treatments had higher prediction accuracy than the MA alone, indicating that the Raman characteristic peaks of phosphate radicals are the main contributing factors of the model; (4) PLSR modeling using the full concentration range can increase the prediction accuracy (R2=0.73~0.89). The modelling accuracy of using RS was the highest, indicating that the baseline shift has a positive effect on the PLSR results; (5) Through the PLSR regression coefficient, 645, 863, 1 070, 1 412 cm-1 were selected as characteristic bands to establish a multiple linear regression (MLR) model, and the coefficient of determination R2 was close to 1. It shows that the selection of characteristic bands can filter out the interference of background light and extract the effective features of phosphate variation. It can be seen from the above results that it is feasible to detect the content of water-soluble phosphorus by Raman spectroscopy quantitatively. Besides reducing the interference of background light and improving the stability of the Raman signal, a method for selecting characteristic bands is important to improve the repeatability and anti-interference ability of the model for high-resolution detection of Raman spectroscopy.