Deep　clustering　attempts　to　capture　the　feature　representation　that　benefits　the　clustering　issue　for　inputs.　Although　the　existing　deep　clustering　methods　have　achieved　encouraging　performance　in　many　research　fields,　there　still　presents　some　shortcomings,　such　as　the　lack　of　consideration　of　local　structure　preservation　and　sparse　characteristics　of　input　data.　To　this　end,　we　propose　the　deep-stacked　sparse　embedded　clustering　method　in　this　paper,　which　considers　both　the　preservation　of　local　structure　and　sparse　property　of　inputs.　The　proposed　network　is　trained　to　capture　the　feature　representation　for　input　data　by　the　guidance　of　clustering　loss　and　reconstruction　loss,　where　the　reconstruction　loss　prevents　the　corruption　of　feature　space　and　guarantees　the　local　structure　preservation.　Besides,　some　sparse　parameters　are　added　to　the　encoder　to　avoid　learning　of　meaningless　features.　Through　simultaneously　minimizing　the　reconstruction　loss　and　cluster　loss,　the　proposed　network　can　jointly　learn　the　clustering　oriented　feature　and　optimize　the　assignment　of　cluster　labels.　Then　we　conduct　amounts　of　comparative　experiments,　which　consist　of　six　clustering　methods　and　four　publicly　available　data　sets.　Eventually,　the　clustering　accuracy,　adjusted　rand　index　and　normalized　mutual　information　are　utilized　as　three　evaluation　metrics　to　provide　a　comparison.　Comprehensive　experiments　validate　the　effectiveness　of　introducing　sparse　property　and　preserving　local　structure　in　our　method.　©　2019　IEEE.