Active　learning,　as　a　technique,　aims　to　effectively　label　specific　data　points　while　operating　within　a　designated　query　budget.　Nevertheless,　the　majority　of　unsupervised　active　learning　algorithms　are　based　on　shallow　linear　representation　and　lack　sufficient　interpretability.　Furthermore,　certain　diversity-based　methods　face　challenges　in　selecting　samples　that　adequately　represent　the　entire　data　distribution.　Inspired　by　these　reasons,　in　this　paper,　we　propose　an　unsupervised　active　learning　method　on　orthogonal　projections　to　construct　a　deep　neural　network　model.　By　optimizing　the　orthogonal　projection　process,　we　establish　the　connection　between　projection　and　active　learning,　consequently　enhancing　the　interpretability　of　the　proposed　method.　The　proposed　method　can　efficiently　project　the　feature　space　onto　a　spanned　subspace,　deriving　an　indicator　matrix　while　calculating　the　projection　loss.　Moreover,　we　consider　the　redundancy　among　samples　to　ensure　both　data　point　diversity　and　enhancement　of　clustering-based　algorithms.　Through　extensive　comparative　experiments　on　six　public　datasets,　the　results　demonstrate　that　the　proposed　method　can　effectively　select　more　informative　and　representative　samples　and　improve　performance　by　up　to　11%.