To　study　the　mechanism　of　motor　deficiency,　therapy　and　rehabilitation　following　stroke,　motor　cortex　network　was　represented　as　a　directed　graph　and　implemented　as　a　dynamical　system.　Complexity　was　used　to　express　the　extent　to　which　a　system　is　both　functionally　segregated　and　functionally　integrated.　Motor　cortex　network　was　optimized　by　pattern　search　to　investigate　the　change　of　connection　under　normal,　stroke　and　rehabilitation　state.　The　simulation　results　show　that　motor　areas　have　strong　connections　in　normal　state.　The　connections　between　motor　areas　decrease　greatly,　but　connections　with　other　areas　and　self-connections　increase　in　stroke.　Stimulation　acting　on　specified　areas　help　to　strengthen　motor　areas　connections　and　weaken　other　connections　to　evolve　the　system　to　normal　state.　It　indicates　the　model　can　reflect　the　plasticity　of　motor　cortex　qualitatively.