Percutaneous coronary intervention (PCI) is a standard treatment for significant coronary artery disease (CAD); however, predicting the need for repeat PCI remains clinically challenging. Traditional survival models, such as the Cox proportional hazards model, rely on static baseline features and fail to capture the dynamic nature of patient trajectories following initial PCI. In this study, we propose DA-RNN-Surv, a novel interpretable survival analysis framework leveraging dual attention recurrent neural networks to incorporate longitudinal electronic health record (EHR) data. Using a real-world cohort of 6,252 PCI patients collected over a 10-year period, DA-RNN-Surv achieved significantly higher predictive performance, with concordance indices ranging from 0.722 at month 3 to 0.894 at month 24, outperforming traditional Cox (0.525 to 0.635) and DeepSurv (0.506 to 0.616) models. Importantly, DA-RNN-Surv provides interpretability through visit-level and feature-level attention weights, identifying key clinical timepoints (e.g., baseline and follow-up months) and influential variables such as medication adherence and comorbidities. These findings demonstrate the clinical value of explicitly modeling temporal information, highlighting DA-RNN-Surv’s potential for enhancing personalized patient management and preventive intervention strategies post-PCI.