Proteomics　research　has　become　one　of　the　most　important　topics　in　the　field　of　life　science　and　natural　science.　At　present,　research　on　protein-protein　interaction　networks　(PPIN)　mainly　focuses　on　detecting　protein　complexes　or　function　modules.　However,　existing　approaches　are　either　ineffective　or　incomplete.　In　this　paper,　we　investigate　detection　mechanisms　of　functional　modules　in　PPIN,　including　open　database,　existing　detection　algorithms,　and　recent　solutions.　After　that,　we　describe　the　proposed　approach　based　on　the　simplified　swarm　optimization　(SSO)　algorithm　and　the　knowledge　of　Gene　Ontology　(GO).　The　proposed　solution　implements　the　SSO　algorithm　for　clustering　proteins　with　similar　function,　and　imports　biological　gene　ontology　knowledge　for　further　identifying　function　complexes　and　improving　detection　accuracy.　Furthermore,　we　use　four　different　categories　of　species　datasets　for　experiment:　fruitfly,　mouse,　scere,　and　human.　The　testing　and　analysis　result　show　that　the　proposed　solution　is　feasible,　efficient,　and　could　achieve　a　higher　accuracy　of　prediction　than　existing　approaches.