Structural　health　monitoring　(SHM)　integrates　advanced　sensor　networks　and　machine　learning　(ML)　technologies,　aiming　to　automatically　extract　and　identify　damage　features　from　sensor　data　of　engineering　structures,　thus　enabling　real-time　assessment　of　structural　integrity　and　early　diagnosis　of　potential　damage.　However,　these　damage　features　often　include　redundant　or　irrelevant　features,　which　pose　challenges　for　effective　feature　extraction　and　damage　diagnosis.　To　solve　these　problems,　a　feature　selection　(FS)　algorithm　based　on　multilayer　cooperative　particle　swarm　optimizer　(MCPSO)　is　proposed.　In　MCPSO,　the　three　learning　strategies　of　midpoint　sample,　random　sample,　and　comprehensive　sample　are　skillfully　mixed　into　the　particle　swarm　optimizer　(PSO),　and　the　hierarchical　structure　is　used　to　update　the　population.　The　damage　feature　subset　is　optimized　by　simulating　the　search　process　of　multilayer　particle　swarm,　and　the　feature　set　most　sensitive　to　structural　damage　is　identified　to　improve　the　accuracy　and　reliability　of　damage　detection.　Taking　the　multidamage　state　monitoring　of　bolted　structure　as　a　verification　case,　the　ultrasonic-guided　waves　(UGWs)　signals　of　bolted　structure　in　different　states　are　collected　by　lead　zirconate　titanate　sensors.　The　experimental　results　show　that　compared　with　the　ML　algorithm,　MCPSO　can　select　a　stable　and　effective　feature　subset　from　the　noise　data　and　realize　the　identification　and　quantification　of　various　damage　states,　such　as　health,　crack,　loosening,　and　loosening-crack　composite　damage,　which　provides　a　universal　method　for　the　technical　development　and　engineering　practice　in　the　field　of　SHM.