With the development of blockchain technology and cryptocurrency systems, transaction behaviors in financial networks have become highly dynamic and exhibit complex evolutionary patterns. Therefore, how to effectively model and understand the dynamic behaviors of nodes in financial networks, so as to enable risk detection and behavior prediction, has become a focal point in current research. In this paper, we propose a Time-aware Channel Fusion Network (TCF-Net) for modeling dynamic evolution and performing link prediction in cryptocurrency transaction networks. Specifically, we employ a time-aware historical neighbor sampling mechanism to extract multimodal features from first-hop interactions, including node attributes, transaction edge features, temporal encodings, and co-occurrence-based neighbor interaction information. Subsequently, we integrate recurrent neural networks for temporal dependency modeling with a multilayer perceptron-based hybrid module to perform channel-wise feature interaction. Experiments conducted on two real-world Bitcoin transaction networks and two phishing detection datasets demonstrate the superiority of our approach in link prediction tasks, achieving notable gains in both expressiveness and robustness. These results highlight the potential of time-aware dynamic graph modeling to enhance blockchain security analytics and user behavior understanding in cryptocurrency networks. Our code is available at https://github.com/ly896/TCF-Net .