The　problem　of　data　with　noise　and　sparsity　of　heterogeneous　information　networks　can　not　be　solved　by　the　traditional　feature　extraction　methods　efficiently　due　to　their　semantics　and　complicated　structure.　Stacked　denoising　auto　encoder　was　introduced　to　learn　the　features　of　sample.　The　relax　strategy　was　employed　to　construct　class　hierarchy　with　high-quality,　and　then　the　nodes　of　the　heterogeneous　information　network　were　classified　and　ranked.　Experimental　results　on　the　dataset　of　DBLP　(digital　bibliography　&　library　project)　show　that　the　method　is　effective,　and　the　precision　of　classification　is　86.3%.　©　2018,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.