By　measuring　the　hysteresis　loop　of　shaft　parts,　the　change　of　its　feature　can　be　used　to　describe　the　surface　hardness　and　case　depth.　It　is　one　of　the　most　promising　technologies　for　nondestructive　testing.　The　key　of　this　technology　is　to　develop　the　measuring　devices　and　research　the　high　precision　identification　method.　This　paper　design　a　hysteresis　loop　measurement　device　for　shaft　parts　based　on　the　closed　magnetic　circuit.　A　Genetic　Algorithm　and　Particle　Swarm　Optimization　(GA-PSO)　hybrid　algorithm　is　proposed　to　identify　the　parameters　based　on　J-A　model,　which　can　realize　the　fast　and　accurate　identification　of　the　global　and　local　characteristic　parameters　of　hysteresis　loop.　According　to　the　measured　hysteresis　loops　of　three　different　kinds　of　steel　material,　the　consuming　time　and　accuracy　of　parameter　identification　are　compared　and　analyzed　among　the　proposed　hybrid　algorithm　and　other　algorithms　(genetic　algorithm;　particle　swarm　optimization;　simulated　annealing　algorithm).　The　experimental　results　show　that　the　minimum　root　mean　square　error　of　the　global　identification　results　of　the　hybrid　algorithm　is　only　0.004　7,　which　is　lower　than　the　corresponding　results　of　other　algorithms.　The　relative　error　of　the　local　feature　parameters　(coercivity　and　residual　magnetic　induction)　identification　results　of　the　hybrid　algorithm　is　less　than　0.35%,　which　is　smaller　than　other　algorithms.　The　experimental　measurements　and　parameters　identification　method　can　be　expected　to　apply　for　the　nondestructive　testing　for　surface　hardened　layer　of　shaft　component,　e.g.,　dowel　pins,　bolts.　©　2017,　Science　Press.　All　right　reserved.