High-resolution processing of seismic signals is crucial for enhancing the ability to characterize underground geological structures in detail and improving the accuracy of thin-layer reservoir identification. Traditional seismic high-resolution algorithms suffer from issues such as poor robustness, low computational efficiency, and neglect of inter-channel structural relationships. Meanwhile, most mainstream deep learning-based high-resolution methods rely on end-to-end networks, lacking guidance from prior information and ignoring differences between data domains, resulting in insufficient generalization capabilities. Therefore, this paper proposes a domain-adaptive knowledge distillation deep learning network for seismic data, DAKD-Net (Domain-Adaptive Knowledge Distillation Network). This method establishes a teacher-student network model using forward simulation datasets as training samples. The teacher network module establishes physical constraints between low- and high-resolution data, extracting high-frequency prior information during the guidance phase to direct the student network module in restoring details without prior conditions. Domain adaptation then generalizes the model to real seismic data, enhancing its generalization capability and structural representation accuracy in actual work area data. Structurally, DAKD-Net adopts a U-net backbone to fully extract spatial structural information across multi-track seismic profiles. Its training mechanism enables prior knowledge transfer through the teacher-student network, allowing high-resolution data recovery without prior information. In application, domain adaptation fine-tuning strategies enhance the network's generalization capability and structural representation effectiveness in real-world work areas. Experimental results demonstrate that the proposed method outperforms traditional approaches and classical deep networks in both vertical resolution and complex structural detail recovery, exhibiting robust performance and practical applicability.