The　paper　constructed　a　depression　classification　model　based　on　emotionally　related　eye-movement　data　and　kernel　extreme　learn　machine　(ELM).　In　order　to　improve　the　classification　ability　of　the　model,　we　use　particle　swarm　optimization　(PSO)　to　optimize　the　model　parameters　(regularization　coefficient　C　and　the　parameter　a　in　the　kernel　function).　At　the　same　time,　in　order　to　avoid　to　be　caught　in　the　local　optimum　and　improve　PSO＇s　searching　ability,　we　use　improved　chaotic　PSO　optimization　algorithm　and　Gauss　mutation　strategy　to　increase　PSO＇s　particle　diversity.　The　classification　results　show　that　the　accuracy,　sensitivity　and　specificity　of　classification　models　without　parameter　optimization　and　Gauss　mutation　strategy　are　80.23%,　80.31%　and　79.43%,　respectively,　while　those　results　of　classification　model　using　improved　chaotic　projection　model　and　Gauss　mutation　strategy　are　improved　to　88.55%,　87.71%　and　89.42%,　respectively.　Compared　with　other　classification　methods　of　depression,　the　proposed　classification　method　has　better　performance　on　depression　recognition.