Graph Neural Networks (GNNs) have made notable advancements in graph-based tasks, such as node classification and link prediction. However, current GNN models still face several limitations. First, they often struggle to fully capture the complex, deep-level relationships between nodes. Second, traditional GNNs rely on static or predefined rules for selecting neighboring nodes, limiting their adaptability to changes in dynamic data distributions. Lastly, the effective integration of information from multiple perspectives remains a challenge when working with multi-view data. To address these issues, this paper introduces an innovative GNN framework, TFGraph. TFGraph enhances the learning capabilities of graph-structured data by integrating attention mechanisms, context-aware aggregation strategies, and channel aggregation methods. Specifically, we propose a reinforcement learning-based module using GATv2 to dynamically adjust the importance threshold of adjacent edges, enabling the accurate identification of critical neighboring nodes. Additionally, we introduce a context-aware aggregation model that combines the PageRank algorithm with a local perception strategy, applying weighted processing to nodes with identical labels. To further improve the integration of multisource information, we design a gated feature fusion mechanism that intelligently combines data from different sub-networks based on their gating weights. Ultimately, TFGraph improves task efficiency by integrating channel convolution mechanisms with Graph Isomorphism Networks (GIN). Experimental results demonstrate that TFGraph outperforms traditional GNN methods, showing significant performance improvements across multiple benchmark datasets.