The　types　of　kernel　function　and　relevant　parameters＇　selection　in　support　vector　machine　(SVM)　have　a　major　impact　on　the　performance　of　the　classifier.　In　order　to　improve　the　accuracy　and　generalization　ability　of　the　model,　we　used　mixed　kernel　function　SVM　classification　algorithm　based　on　the　information　entropy　particle　swarm　optimization　(PSO):　on　the　one　hand,　the　generalization　ability　of　classifier　is　effectively　enhanced　by　constructing　a　mixed　kernel　function　with　global　kernel　function　and　local　kernel　function;　on　the　other　hand,　the　accuracy　of　classification　is　improved　through　optimization　for　related　kernel　parameters　based　on　information　entropy　PSO.　Compared　with　PSO-RBF　kernel　and　PSO-mixed　kernel,　the　improved　PSO-mixed　kernel　SVM　can　effectively　improve　the　classification　accuracy　through　the　classification　experiment　on　biomedical　datasets,　which　would　not　only　prove　the　efficiency　of　this　algorithm,　but　also　show　that　the　algorithm　has　good　practical　application　value　in　biomedicine　prediction.