[Purpose/significance] For the purpose of pursuing harmonious development for the entire society, governments need to undertake the predominant role of grasping the transformation node of social demands, identifying the theme of social demands, and tracking the evolution trend of the theme.[Method/process] To this end, this paper proposed an innovative method based on semantic dependence network, which is able to identify related topics and analyze evolutionary paths. Firstly, we constructed the source-target word pairs by the full combination method for the ame document core terms. The dynamic semantic dependency network was  constructed by time interval division and Word2Vec model. Secondly, we employed the community discovery algorithm to identify the semantically dependent web subcommunities in each interval. We also adopted the PageRank algorithm to stand out the topic tags of each sub?community. By measuring similarities of the adjacent time intervals, we drew the evolutionary relationship of the target
topic, illustrating the whole life circle from topic generation, splitting, fusion to eventually decay. Finally, we empirically verified the proposed model by the data collected from governor's mailbox of Gansu province. We evaluated our results with several key indicators, namely accuracy rate, recall rate and F1 value. [Result/conclusion] All the three indicators were above the 0.5 cut- off values, proving significant optimization effects have been achieved. This research built a panoramic view of the public concerns, providing insights for exploring other social text mining methods, contributing to the successful practice of intelligent governance driven by big data techniques.