Obtaining　excellent　prediction　accuracy　in　the　high-dimensional　partially　linear　model　is　particularly　important.　However,　it　is　difficult　to　achieve　due　to　the　complex　relationship　between　nonparametric　covariates　and　the　response.　Irrelevant　covariates　and　unimportant　data　also　commonly　attenuate　the　prediction　performance　of　the　model.　Further,　high-dimensional　data　analysis　is　challenging　for　modern　statistical　studies.　To　overcome　these　difficulties,　we　propose　the　Double　Sparsity　Garrotized　Kernel　Machine　(DSGKM)　method　with　an　efficient　algorithm　and　its　adjusted　version　for　prediction.　Specifically,　we　estimate　the　nonparametric　components　using　the　kernel　machine　technique,　and　impose　L1-norm　penalties　simultaneously　to　select　relevant　covariates　and　retain　the　representative　data　in　the　final　model.　Besides,　the　convergence　analysis　of　the　adjusted　algorithm　is　conducted.　The　advantages　of　our　method　are:　(i)　to　sufficiently　capture　the　complex　relationship　between　nonparametric　covariates　and　the　response;　(ii)　to　identify　relevant　covariates　and　select　representative　data;　and　(iii)　to　achieve　higher　computational　efficiency,　especially　the　situations　when　both　parametric　and　nonparametric　components　are　high-dimensional.　Results　on　both　simulated　and　real　data　show　that　the　proposed　method　outperforms　existing　methods,　even　when　outliers　exist.