A Domain Generation Algorithm (DGA) employs botnets to generate domain names through a communication link between the C&C server and the bots. A DGA can generate pseudo-random AGDs (algorithmically generated domains) regularly, a handy method for detecting bots on the C&C server. Unlike current DGA detection methods, AGDs can be identified with lightweight, promising technology. DGAs can prolong the life of a viral operation, improving its profitability. Recent research on the sensitivity of deep learning to various adversarial DGAs has sought to enhance DGA detection techniques. They have character- and word-level classifiers; hybrid-level classifiers may detect and classify AGDs generated by DGAs, significantly diminishing the effectiveness of DGA classifiers. This work introduces WordDGA, a hybrid RCNN-BiLSTM-based adversarial DGA with strong anti-detection capabilities based on NLP and cWGAN, which offers word- and hybrid-level evasion techniques. It initially models the semantic relationships between benign and DGA domains by constructing a prediction model with a hybrid RCNN-BiLSTM network. To optimize the similarity between benign and DGA domain names, it modifies phrases from each input domain using the prediction model to detect DGA family categorizations. The experimental results reveal that dodging numerous wordlists and mixed-level DGA classifiers with training and testing sets improves word repetition rate, domain collision rate, attack success rate, and detection rate, indicating the usefulness of cWGAN-based oversampling in the face of adversarial DGAs.