[Purpose/significance] Exploring the clustering evolution in the process of domain knowledge development can help to reveal the characteristics and rules of knowledge clustering, this is great significance to master the clustering rules of correlation knowledge in the development and evolution process.[Method/process] Based on the idea of complex network, this paper constructed the time series domain knowledge networks in accordance with the occurred-value of tags adjacency relation. That is, according to the network motif theory, this paper dynamically tracked and analyzed the domain knowledge networks by the analysis method of network clustering coefficient. Then, by combining with the network density, the characteristic path length, the node degree value, the triadic closure and other indicators, this article analyzed the clustering evolution in the process of domain knowledge development from random factors, degree correlation, and adjacent correlation.[Result/conclusion] The results show:①Domain knowledge in the development process always keeps a higher clustering. ②The clustering of domain knowledge includes both randomness and structuration (non-randomness). ③The dynamic status of domain knowledge clustering evolves between small-world network and scale-free network waveringly. ④The clustering status of domain knowledge shows a certain difference between the whole network and local nodes.
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