Graph Neural Networks （GNNs） have achieved significant success in node classification tasks， but their performance typically relies on abundant labeled data in majority classes， which may lead to representation bias for nodes belonging to minority classes with scarce labels. Traditional oversampling techniques mitigate class imbalance by replicating minority samples， but they can easily lead to local neighborhood overfitting. Recent approaches have attempted to synthesize new nodes based on minority-class anchors， but they have failed to fully exploit relationships between minority and adjacent classes， resulting in blurred class boundaries in the generated samples. To address the above challenges， a Competitive loss-driven Generative imbalanced node classification algorithm （GraphCG） was proposed. A feature-structure collaborative auxiliary node selection mechanism was designed to precisely identify auxiliary points from neighboring classes that can enhance class boundaries. Furthermore， a competitive boundary-constrained loss function was constructed to enforce the maintenance of geometric boundary separability between generated nodes and majority classes in the embedding space. Experimental results showed that， compared to current state-of-the-art methods， GraphCG achieved significant improvements across multiple class-imbalanced datasets.GraphCG not only enhances data diversity but also improves class separability， preventing minority classes from being overshadowed by majority classes.