With the development of modern information technology and global information network, more and more knowledge service providers are transforming to digital platform service mode. As the intermediary of the market, the enterprise gathers the supply and demand information of knowledge demanders and knowledge suppliers together through the virtual network platform, so as to achieve the maximum matching of the interests of both sides. For the actual knowledge service supply and demand matching problem, considering that the cardinality of matching objects may be large. Firstly, we propose a multi-attribute ordered clustering method based on probabilistic linguistic information, which makes the two-sided matching problem in the same ranked class. Secondly, considering that the preference information is affected by the subject’s psychology, based on the disappointment theory, the disappoint-satisfaction function is constructed to modify the preference information. In addition, the expected preference value is defined to ensure that the matching parties are mutually satisfied. An example shows that the method is effective and practical. The method proposed in this paper extends the application of knowledge service supply and demand matching method, and provides a theoretical basis for improving the efficiency and personal satisfaction of knowledge service supply and demand matching.