The　hierarchical　decentralized　control　problem　of　building　structure　vibration　control　is　studied　in　this　paper.　Firstly,　The　associated　coupling　between　subsystems　is　eliminated　by　setting　the　global　controller.　Secondly,　Lyapunov　stability　theory　and　RBF　neural　network　theory　are　employed　to　design　the　adaptive　control　law　which　depends　only　on　the　displacement　and　the　velocity　response　of　relevant　subsystem,　and　the　parameters　of　adaptive　RBF　neural　network　local　sub-controller　is　optimized　through　using　the　differential　evolution　(DE)　algorithm.　And　then,　the　adaptive　RBF　neural　network　hierarchical　decentralized　control　(ARBFHDC)　algorithm　is　established　for　the　building　structure　vibration　control.　The　ASCE　9-story　Benchmark　building　is　selected　as　a　numerical　example　to　evaluate　the　control　performances　of　the　hierarchical　decentralized　control.　Numerical　simulation　results　indicate　that　the　ARBFHDC　algorithm　is　suitable　for　hierarchical　decentralized　control　strategy　under　different　seismic　excitations,　it　can　perform　up　to　a　superior　control　performance　when　comparing　with　traditional　centralized　control,　and　can　guarantee　the　actuators　of　each　subsystem　are　operating　at　maximum　efficiency.　©　2018,　Editorial　Office　of　China　Civil　Engineering　Journal.　All　right　reserved.