Abstract: The RNA-dependent RNA polymerase (RdRp) plays a crucial role in dengue virus replication and serves as a key target for antiviral drug development. In this study, artificial intelligence methods were employed to construct the three-dimensional structure of the DENV-4 RdRp/inhibitor complex, followed by molecular docking to determine the active conformations of 31 compounds. Subsequently, Comparative Molecular Field Analysis (CoMFA) was employed to establish a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. The CoMFA model derived from 25 training set compounds yielded a cross-validated coefficient (q²) of 0.777, a non-cross-validated correlation coefficient (r²) of 0.993, and a standard error of estimate (SEE) of 0.129. For the external test set of six compounds, the predictive correlation coefficient (r²_pred) reached 0.85, indicating strong fitting accuracy and high predictive power. Analysis of CoMFA contour maps elucidated key structural determinants related to steric and electrostatic fields that influence inhibitory activity. Guided by these insights, a series of thiophene-biphenyl derivatives with promising predicted potency were rationally designed, offering a theoretical foundation for the further optimization of potent RdRp inhibitors.