Domain　generation　algorithms　(DGAs)　use　specific　parameters　as　random　seeds　to　generate　a　large　number　of　random　domain　names　to　prevent　malicious　domain　name　detection.　This　greatly　increases　the　difficulty　of　detecting　and　defending　against　botnets　and　malware.　Traditional　models　for　detecting　algorithmically　generated　domain　names　generally　rely　on　manually　extracting　statistical　characteristics　from　the　domain　names　or　network　traffic　and　then　employing　classifiers　to　distinguish　the　algorithmically　generated　domain　names.　These　models　always　require　labor　intensive　manual　feature　engineering.　In　contrast,　most　state-of-the-art　models　based　on　deep　neural　networks　are　sensitive　to　imbalance　in　the　sample　distribution　and　cannot　fully　exploit　the　discriminative　class　features　in　domain　names　or　network　traffic,　leading　to　decreased　detection　accuracy.　To　address　these　issues,　we　employ　the　borderline　synthetic　minority　over-sampling　algorithm　(SMOTE)　to　improve　sample　balance.　We　also　propose　a　recurrent　convolutional　neural　network　with　spatial　pyramid　pooling　(RCNN-SPP)　to　extract　discriminative　and　distinctive　class　features.　The　recurrent　convolutional　neural　network　combines　a　convolutional　neural　network　(CNN)　and　a　bi-directional　long　short-term　memory　network　(Bi-LSTM)　to　extract　both　the　semantic　and　contextual　information　from　domain　names.　We　then　employ　the　spatial　pyramid　pooling　strategy　to　refine　the　contextual　representation　by　capturing　multi-scale　contextual　information　from　domain　names.　The　experimental　results　from　different　domain　name　datasets　demonstrate　that　our　model　can　achieve　92.36%　accuracy,　an　89.55%　recall　rate,　a　90.46%　F1-score,　and　95.39%　AUC　in　identifying　DGA　and　legitimate　domain　names,　and　it　can　achieve　92.45%　accuracy　rate,　a　90.12%　recall　rate,　a　90.86%　F1-score,　and　96.59%　AUC　in　multi-classification　problems.　It　achieves　significant　improvement　over　existing　models　in　terms　of　accuracy　and　robustness.