Deep　clustering　attempts　to　capture　the　feature　representation　that　benefits　the　clustering　issue.　Although　the　existing　deep　clustering　methods　have　achieved　encouraging　performance　in　many　research　fields,　there　still　present　some　shortcomings,　such　as　the　lack　of　consideration　of　local　structure　retention　and　sparse　characteristics　of　input　data.　To　this　end,　we　propose　a　deep　stacked　sparse　embedded　clustering　method　in　this　paper,　which　considers　both　the　local　structure　preservation　and　sparse　property　of　inputs.　The　proposed　method　is　trained　to　capture　the　feature　representation　for　an　input　data　by　the　guidance　of　the　clustering　and　reconstruction　loss,　where　the　reconstruction　loss　prevents　the　corruption　of　feature　space　and　preserve　the　local　structure.　Besides,　sparse　constraint　is　added　to　the　encoder　to　avoid　learning　of　unimportant　features.　Through　simultaneously　minimizing　the　reconstruction　and　clustering　loss,　the　proposed　method　is　able　to　jointly　learn　the　clustering　oriented　features　and　optimize　the　assignment　of　cluster　labels.　Then　we　conduct　amounts　of　comparative　experiments,　which　consists　of　seven　clustering　methods　and　six　publicly　available　image　data　sets.　Eventually,　comprehensive　experiments　validate　the　effectiveness　of　introducing　sparse　property　and　preserving　local　structure　in　the　proposed　method.