The goal of few-shot learning is to learn a better performing model using only a small amount of data. Therefore, the quality of a small amount of data (the few-shot dataset) is a key factor in the effectiveness of model learning. It is especially important to assess the quality of the few-shot datasets before the few-shot learning. Existing data quality assessment methods are more likely to assess the quality of datasets qualitatively from multiple subjective dimensions and indicators, and there are few methods that assess the quality of few-shot datasets from both quantitative and qualitative perspectives together. Therefore, this paper proposes a quality assessment method of few-shot datasets based on the fusion of quantity and quality, which enriches the research on few-shot learning methods. First, from the quantitative perspective, the method introduces two key factors affecting the minimum sample size of few-shot datasets, namely, sample complexity and category diversity. Then, from the qualitative perspective, based on the information entropy theory, it establishes a quality assessment method that integrates multiple objective dimensions, which include intra-class feature consistency, inter-class feature dissimilarity, and task relevance. Finally, experimental analyses are carried out on several popular few-shot datasets under three scenarios to verify the effectiveness of the proposed method.