Potassium thiocyanate (KSCN) is used as the internal standard. And principal component analysis (PCA) is utilized to reduce the dimension. Quantitative analysis model, that is, support vector regression (SVR), is established by support vector machine (SVM) algorithm. Meanwhile, three parameter optimization methods, that is grid search (GS), genetic algorithm (GA) and particle swarm optimization (PSO), are used to fulfill quantitative analysis of single and mixed solutions of pyrene and phenanthrene. The research results show that the use of KSCN as the internal standard improves the accuracy of the quantitative mensuration results. The modeling speed is improved by PCA dimensionality reduction. The average relative errors (AREs) of pyrene solution predicted by three optimized models are within 7.6%. The AREs of phenanthrene solution prediction are within 11.3%. The three parameter optimization methods have similar prediction results for the same sample, but the operating rate of GS is the fastest. Considering the errors and analysis speed, the best results of phenanthrene and anthracene mixed solution are obtained by GS-SVR model. Surface-enhanced Raman spectroscopy (SERS) technology combined with SVM algorithm is expected to actualize quantitative analysis of polycyclic aromatic hydrocarbons.