Protein remote homology detection and fold recognition are key computational biology problems that are integral to better understanding protein function, evolution, and drug discovery. Recent advances in deep learning techniques are potentially beneficial for solving these problems with greater accuracy and computational efficiency. In this study, we investigated the use of transfer learning and attention in deep learning models that improve protein remote homology detection and fold recognition performance. We compared these deep learning models to conventional methods, and our evaluation method was the Matthews Correlation Coefficient (MCC). Overall, our findings show that transfer learning and deep learning models with attention outperformed conventional methods consistently with greater accuracy and stability. In this paper, we emphasize the transformative potential of deep learning in the field of protein analysis, which demonstrates substantial significance for bioinformatics applications and therapeutic utilization.