In　this　article,　we　consider　the　complete　independence　test　of　high-dimensional　data.　Based　on　Chatterjee　coefficient,　we　pioneer　the　development　of　quadratic　test　and　extreme　value　test　which　possess　good　testing　performance　for　oscillatory　data,　and　establish　the　corresponding　large　sample　properties　under　both　null　hypotheses　and　alternative　hypotheses.　In　order　to　overcome　the　shortcomings　of　quadratic　statistic　and　extreme　value　statistic,　we　propose　a　testing　method　termed　as　power　enhancement　test　by　adding　a　screening　statistic　to　the　quadratic　statistic.　The　proposed　method　do　not　reduce　the　testing　power　under　dense　alternative　hypotheses,　but　can　enhance　the　power　significantly　under　sparse　alternative　hypotheses.　Three　synthetic　data　examples　and　two　real　data　examples　are　further　used　to　illustrate　the　performance　of　our　proposed　methods.