Motivated　by　the　high-dimensional　compositional　data　appearing　in　many　fields,　this　paper　addresses　the　problem　of　large　covariance　estimation　for　compositional　data.　Firstly,　we　introduce　the　hard　thresholding　estimator　to　approximate　the　sparse　basis　covariance　matrix　which　is　relevant　with　compositional　data.　Then　the　upper　error　bounds　are　measured　by　the　general　matrix　lv,w-norm　and　the　general　entrywise　Lv,w-norm　respectively　with　v,w∈[1,∞]　in　terms　of　probability.　Finally,　numerical　simulations　and　real　datasets　application　demonstrate　that　our　estimator　is　close　to　the　oracle　estimator　and　outperforms　the　COAT　estimator　proposed　by　Cao　et　al.　(2019).　©　2024　Elsevier　B.V.