Polystyrene binding peptides play a key role in the immobilization process. The correct identification of polystyrene binding peptides is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a 5-fold cross validation test, this model achieves 90.38%, 84.62%, 87.50% and 0.90 SN, SP, ACC and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model PSBP-SVM.