In recent years, graph convolutional networks (GCNs) based on skeletal data have been widely applied in action recognition. However, challenges persist in effectively constructing graph topologies and aggregating skeletal information. Conventional methods usually employ fixed, static skeletal topologies, which struggle to adapt to subtle variations in complex actions. Adaptive topology methods, although capable of dynamically adjusting the graph structure, often do so at the expense of the effectiveness of the original skeletal information. To address these issues, this paper proposes a Topology-aware Discriminative Graph Convolutional Network (TD-GCN) designed to enhance the modeling flexibility for complex actions and improve adaptive topology construction. Additionally, the paper introduces a Spatiotemporal Feature Discrimination Head (STFD-Head) that leverages contrastive learning to dynamically identify and rectify ambiguous samples in the feature space, further boosting the model's discriminative capability in challenging scenarios. Experimental results demonstrate that the proposed method performs excellently on the NTU RGB + D, NTU RGB + D 120, and NW-UCLA datasets. Notably, on the large-scale and complex NTU RGB + D 120 dataset, the method achieves accuracies of 90.7% on the X-Sub benchmark and 91.9% on the X-Set benchmark.