Malware detection remains a critical challenge due to the increasing use of code obfuscation, packing, and wrapping techniques, which hinder traditional static analysis methods. Dynamic analysis, particularly through the examination of Application Programming Interface (API) call sequences, has emerged as an effective approach for identifying malicious behaviors. However, existing deep learning models often struggle to generate high-quality representations of API calls and are unable to handle previously unseen APIs, thereby limiting detection performance and model generalization. To address these challenges, we propose a novel malware dynamic analysis framework that leverages GPT-4 prompt engineering to generate descriptive text for each API call within a sequence. These descriptions are then encoded using a pre-trained BERT model to produce rich, knowledge-enhanced representations of API sequences. Our method not only incorporates external knowledge for improved semantic understanding but also enables the representation of unknown API calls, thus enhancing generalization. We further design a CNN-based classifier to extract features from the enriched representations for malware detection and classification. Extensive experiments on five benchmark datasets demonstrate that our approach outperforms state-of-the-art methods, achieving superior detection accuracy and generalization across different datasets. Especially, the detection accuracy on the Catak dataset increased by 12.38%, which highlights the significant improvement of our method in challenging scenarios.