Few-shot relation extraction has attracted significant attention due to its potential to identify new relations when training samples are scarce. Previous studies demonstrate that meta-learning techniques can significantly enhance models' adaptability in few-shot scenarios. Most existing meta-learning methods generalize to unseen relations by constructing effective prototype representations for each relation. However, meta-learning techniques often require a large amount of labeled data during the meta-training stage, which contradicts the initial purpose of addressing the issue of insufficient labeled data. Current methods usually fail to model both intra-class similarity and inter-class difference sufficiently, resulting in fuzzy class prototypes when distinguishing between similar but slightly different relations. Therefore, we propose a novel task, low-resource few-shot relation extraction (LR-FSRE), that explores minimizing the usage of labeled data while maximizing the acquisition of task meta-knowledge, and we design a novel graph-based adaptive discrimination network that optimizes relation prototypes in both tasks. Specifically, we effectively represent class prototypes using the topological information between instance and relation-level representation. Then, the relation discrimination network considers the difference between similar classes to further improve the recognition ability of relation classes. Finally, a debiased optimization strategy is employed to optimize the learning processes of task-general and task-specific knowledge. Experimental results show that our proposed framework outperforms the state-of-the-art methods in FSRE and LR-FSRE tasks, and achieves significant improvement in accuracy across challenging cross-domain and similarity relation discrimination scenarios.