With the rapid growth and increasing complexity of malicious software, traditional methods of malware detection have become inadequate to meet current security demands. A deep learning-based ensemble framework was proposed to address limitations of traditional malware detection methods. The framework integrated GPT-4, BERT, and a hybrid CNN-LSTM architecture. First, contextual explanations of API calls were generated by GPT-4 and subsequently encoded into semantic vectors using BERT. The hybrid CNN-LSTM was then employed to capture both local patterns and long-term dependencies within behavioral sequences. Experiments conducted on the mal-api-2019 dataset achieved an accuracy of 97.53%, a precision of 96.80%, a recall of 97.50%, and an F₁-score of 97.15%, outperforming state-of-the-art approaches. Further validation on the VirusShare dataset confirmed superior generalization capability. The work provides an innovative solution for combating evolving malware threats through the synergistic integration of natural language processing and deep learning techniques.