Abstract: 【Purpose/significance】In response to the existing research on literature topic mining, which has fewer index dimensions for keyword screening and the problem of uniqueness in the topic evolution and ranking methods of literature, this paper proposes a highvalue hot topic mining method based on time series clustering and skyline algorithm.【Method/process】Firstly, the paper stratifies the value of keywords through RFM model to obtain keywords with high value levels. Next, the initial thematic clusters are re-clustered to reveal the evolutionary phenomenon of similar developmental characteristics existing among different themes. At the same time, we ex⁃ tract the characterization indexes of the relative importance of topics, and realize the scientific ranking of topics with the help of sky⁃ line algorithm and principal component analysis. Finally, the paper processed and mined the data of journal literature related to "ur⁃ ban and rural community supply and demand services" from 1998 to 2022 on China Knowledge Network.【Result/Conclusion】The new method proposed in this paper integrates the temporal dimension and value attributes of keywords, and gives a more systematic topic mining method that integrates topic identification, evolution and ranking. The comparative analysis of the results shows that this method can accurately and quickly identify high-value hot topics and comprehensively evaluate the topic hotness in multiple dimensions【. Innovation/limitation】This research's calculation methods for measuring keyword value and topic popularity mainly focus on the optimization of evaluation criteria and measurement indicators, and do not cover all possible ele⁃ ments that might affect these indicators.