Memory and language are important high-level cognitive functions of humans, and the study of conceptual representation of the human brain is a key approach to reveal the principles of cognition. However, this research is often constrained by the availability of stimulus materials. The research on concept representation often needs to be based on a standardized and large-scale database of conceptual semantic features. Although Western scholars have established a variety of English conceptual semantic feature datasets, there is still a lack of a comprehensive Chinese version. In the present study, a Chinese Conceptual semantic Feature Dataset (CCFD) was established with 1,410 concepts including their semantic features and the similarity between concepts. The concepts were grouped into 28 subordinate categories and seven superior categories artificially. The results showed that concepts within the same category were closer to each other, while concepts between categories were farther apart. The CCFD proposed in this study can provide stimulation materials and data support for related research fields. All the data and supplementary materials can be found at https://osf.io/ug5dt/.

记忆和语言是人类重要的高级认知功能，研究人脑的概念表征是揭示认知原理的关键途径。然而，这项研究往往受到刺激材料可用性的限制。概念表征的研究往往需要基于一个标准化的、大规模的概念语义特征数据库。西方学者虽然建立了多种英文概念语义特征数据集，但仍缺乏全面的中文版本。在本研究中，建立了一个中文概念语义特征数据集（CCFD），其中包含 1,410 个概念，包括它们的语义特征和概念之间的相似性。这些概念被人为地分为28个下级和7个上级。结果表明，同一类别内的概念彼此更接近，而类别之间的概念相距更远。本研究提出的CCFD可为相关研究领域提供增产材料和数据支持。所有数据和补充材料都可以在 https://osf.io/ug5dt/ 找到。