Abstract: Public opinion events in social networks have a bearing on social harmony and stability. Analyzing the evolution trend of events so as to manage and control them is able to reduce the impact of malignant online public opinion. However, the lack of labelled data and the limited relevant resources makes the effective management of online public opinion challenging and complicated. To solve those difficulties, we propose a learning-to-rank algorithm for the events evolution trend evaluation, which makes full use of the expert knowledge in the labelled data and the correlation between labelled and unlabelled data to select important public opinion for management, in turn, improves the management efficiency. Firstly, based on the experiences and demands of public opinion management, we design a measurable, accessible and meaningful hierarchical index system, which is derived from the three most important factors of events, for evolution trend evaluation. Secondly, we build an evaluation model for evolution trend evaluation based on the graph convolutional network. Specifically, our model uses the local sensitive Hash algorithm to mine the structural information from the data node’s neighborhood and generates the mixed feature of the data node and its neighbor. Finally, we design different loss functions for the labelled and unlabelled data respectively, in order to realize the comprehensive utilization of the expert knowledge in the labelled data and the spatial structure information in the unlabelled data. We verify the efficiency of the proposed model on public datasets MQ 2007-semi and MQ 2008-semi. We also build a real-world public opinion event dataset to verify the practicability and generalization of the proposed algorithm. The experimental results show that the proposed model can evaluate the public opinion event evolution trend with limited expert knowledge, and provide decision support for public opinion event management with limited resources.