In　this　paper,　we　focus　on　learning　semantic　representations　for　large-scale　highly　abstract　sketches　that　were　produced　by　the　practical　sketch-based　application　rather　than　the　excessively　well　dawn　sketches　obtained　by　crowd-sourcing.　We　propose　a　dual-branch　CNN-RNN　network　architecture　to　represent　sketches,　which　simultaneously　encodes　both　the　static　and　temporal　patterns　of　sketch　strokes.　Based　on　this　architecture,　we　further　explore　learning　the　sketch-oriented　semantic　representations　in　two　practical　settings,　i.e.,　hashing　retrieval　and　zero-shot　recognition　on　million-scale　highly　abstract　sketches　produced　by　practical　online　interactions.　Specifically,　we　use　our　dual-branch　architecture　as　a　universal　representation　framework　to　design　two　sketch-specific　deep　models:　(i)　We　propose　a　deep　hashing　model　for　sketch　retrieval,　where　a　novel　hashing　loss　is　specifically　designed　to　further　accommodate　both　the　abstract　and　messy　traits　of　sketches.　(ii)　We　propose　a　deep　embedding　model　for　sketch　zero-shot　recognition,　via　collecting　a　large-scale　edge-map　dataset　and　proposing　to　extract　a　set　of　semantic　vectors　from　edge-maps　as　the　semantic　knowledge　for　sketch　zero-shot　domain　alignment.　Both　deep　models　are　evaluated　by　comprehensive　experiments　on　million-scale　abstract　sketches　produced　by　a　global　online　game　QuickDraw　and　outperform　state-of-the-art　competitors.