Due to its extensive applicability in various downstream domains, few-shot named entity recognition (NER) has attracted increasing attention, particularly in areas where acquiring sufficient labeled data poses a significant challenge. Recent studies have highlighted the potential of knowledge graphs (KGs) in enhancing natural language processing (NLP) tasks. However, a comprehensive understanding of whether and how KGs can effectively improve the NER performance under low-resource conditions remains elusive. In this paper, for the first time, we quantitatively investigate the effects of different kinds of extra KG features for few-shot NER. We enable our analysis by aggregating extra KG features into an NER framework. Through extensive experiments, we find that incorporating class features yields the best performance. To fully explore the potential of class features from KGs, we propose a novel network architecture, named KGen, to jointly leverage KG-based knowledge from both the input sentence side and the label semantic side for few-shot NER.The efficacy of our proposed method is validated through extensive experiments on five challenging datasets.