Using　kernel　methods,　Lepski　and　Willer　study　a　convolution　structure　density　model　and　establish　adaptive　and　optimal　Lp　risk　estimations　over　an　anisotropic　Nikol＇skii　space　(Lepski,　O.;　Willer,　T.　Oracle　inequalities　and　adaptive　estimation　in　the　convolution　structure　density　model.　Ann.　Stat.　2019,　47,　233-287).　Motivated　by　their　work,　we　consider　the　same　problem　over　Besov　balls　by　wavelets　in　this　paper　and　first　provide　a　linear　wavelet　estimate.　Subsequently,　a　non-linear　wavelet　estimator　is　introduced　for　adaptivity,　which　attains　nearly-optimal　convergence　rates　in　some　cases.