Graph Neural Network （GNN） commonly faces the Under?Reachability problem （Under?Reach） when processing graph data with sparse and unevenly distributed labeled nodes， which means that distant unlabeled nodes cannot effectively receive supervised signals due to topological constraints， leading to limited model generalization ability. Although existing methods can partially address this issue， they still have limitations， including over?smoothing， high computational complexity， and noise sensitivity. Therefore， a GNN framework for topology semantic dual?domain collaboration， named TriMix， was proposed to address the above challenges through three key improvements. First， a dynamic mixing ratio mechanism was designed to adjust mixing weights between pseudo?labels and ground?truth labels adaptively across training epochs， relying on ground?truth labels for stable convergence in the early stage while incorporating high?confidence pseudo?labels gradually in the latter?stage training for decision boundary expansion. Second， a topology?semantic dual?domain collaborative node?weighted sampling strategy was constructed by integrating node degree， PageRank value， and feature similarity， so as to quantify node importance and optimize information propagation paths， enhancing the reachability of low?centrality nodes. Third， a contrastive learning module was implemented with a triple?level negative sample generation strategy of category?driving， feature?similarity weighting， and pseudo?label guidance， to refine the discriminability between positive and negative samples in the embedding space， thereby enhancing the semantic understanding of unlabeled data. Experimental results on benchmark datasets such as Cora and PubMed showed that TriMix achieved node classification accuracy 2.1% to 4.4% higher than baseline models like Graph Convolutional Network （GCN） and Graph ATtention network （GAT）， with improved F1?score and generalization ability. The TriMix framework significantly improves learning efficiency on sparsely labeled graph data through dual?domain collaboration of topological structure and semantic features， providing a new approach to node classification tasks in complex graph structures.