Protein-protein　interaction　plays　a　fundamental　role　in　many　biological　processes　and　diseases.　Characterizing　protein　interaction　sites　is　crucial　for　the　understanding　of　the　mechanism　of　protein-protein　interaction　and　their　cellular　functions.　In　this　paper,　we　proposed　a　method　based　on　integrated　support　vector　machine　(SVM)　with　a　hybrid　kernel　to　predict-protein　interaction　sites.　First,　a　number　of　features　of　the　protein　interaction　sites　were　extracted.　Secondly,　the　technique　of　sliding　window　was　used　to　construct　a　protein　feature　space　based　on　the　influence　of　the　adjacent　residues.　Thirdly,　to　avoid　the　impact　of　imbalance　of　the　data　set　on　prediction　accuracy,　we　employed　boost-strap　to　re-sample　the　data.　Finally,　we　built　a　SVM　classifier,　whose　hybrid　kernel　comprised　a　Gaussian　kernel　and　a　Polynomial　kernel.　In　addition,　an　improved　particle　swarm　optimization　(PSO)　algorithm　was　applied　to　optimize　the　SVM　parameters.　Experimental　results　show　that　the　PSO-optimized　SVM　classifier　outperforms　existing　methods.