Procymidone, as a new type of agricultural fungicide, has the effect of preventing agricultural products from being affected by pests and diseases, but it is easy to be used improperly to harm the environment and human health during the application process. In order to strengthen the detection of procymidone pesticides, this study uses laser induced breakdown spectroscopy (LIBS) to quantitatively detect the content of procymidone in solution. In order to prepare different density of procymidone samples, this study mixed the ingredient content of 98% procymidone powder with xylene in different proportions and completely dissolved. Since liquid samples are easy to spill and cause dangers during laser striking, so this experiment converted the liquid samples into solid samples, used the graphite to adsorb the procymidone, and then used the eight-channel high-precision spectrometer to collect the LIBS spectrum of the sample, and applied different pretreatment methods to pretreat the spectral data. So as to improve the detection accuracy of procymidone, this research chose the strongest chlorine signalthe in two channels (744.555~935.843, 893.107~1 057.058 nm) and spectral data were preprocessed with normalization, baseline correction, standard normal variable transformation and multiplicative scatter correction methods respectively, and PLS method was used to model. After comparing the data of each pretreatment method, considering the comprehensive consideration, the Baseline method was selected as the optimal pretreatment method. Based on the baseline preprocessing method, uninformed variable elimination (UVE) combined with competitive adaptive reweighted sampling (CARS) algorithm was used to eliminate the wavelength variable without information, and screen out the important wavelength variables related to procymidone, and finally the partial least squares regression was used to establish a quantitative prediction model of procymidone content in solution. The modeling results showed that after the spectral preprocessing and optimized by VUE-CARS method, the number of original 4 096 wavelength variables reduced to 13, and the variable compression rate was 99.68%. The PLS model was established after the UVE-CARS variable was optimized. The correction set and prediction set determination coefficient and root mean square error were 0.990 5, 0.66, and 0.990 3, 0.67, respectively. The model performance was better than the PLS model established by the original spectrum. The results showed that it is feasible to detect the procymidone content quantitatively in the solution by using the coaxial double pulse LIBS technique. After screened by UVE and CARS methods, the characteristic variables and related influence variables of procymidone can be effectively extracted. The redundancy and noise influences variables can be eliminated effectively. The quantitative analysis model can be simplified and the stability of the quantitative analysis model can be improved.