The　functional　brain　network　(FBN)　classification　based　on　convolutional　neural　networks　(CNN)　is　of　great　significance　for　discovery　and　diagnosis　of　brain　diseases,　and　has　attracted　increasing　attention.　However,　all　the　CNN　architectures　of　current　studies　mainly　depend　on　hand-crafted,　which　are　labor-intensive　and　unreliable.　To　solve　it,　we　propose　a　neural　architecture　search　(NAS)　method　based　on　particle　swarm　optimization,　to　automatically　design　the　CNN　architecture　for　FBN　classification.　Specifically,　this　method　includes　three　phases,　namely　the　individual　expression　phase,　the　individual　evaluation　phase,　and　the　individual　update　phase.　In　the　first　phase,　we　treat　the　neural　architecture　as　the　individual　in　particle　swarm.　The　individual　vector　consists　of　six　elements,　and　the　value　of　each　element　represents　the　number　of　a　special　convolution　operation.　The　six　special　convolution　operations　can　effectively　extract　brain　network　multilevel　topological　features.　In　the　second　phase,　we　propose　a　novel　surrogate-assisted　predictor　to　evaluate　the　fitness　of　the　individuals　more　efficiently.　In　the　last　phase,　we　apply　the　predicted　fitness　to　acquire　the　historical　optimum　of　each　individual　and　the　global　optimum　of　the　population,　and　use　them　to　update　all　individuals　in　the　particle　swarm.　The　second　and　third　phases　are　repeatedly　performed　until　the　end　condition　is　met.　Experiments　on　benchmark　datasets　demonstrate　that　the　CNN　architecture　searched　by　our　method　achieves　better　classification　performance　than　state-of-the-art　hand-crafted　CNN　architectures.