The case-related public opinion summarization is the task of extracting a few sentences that can summarize the subject information from some case-related news documents. The case-related public opinion summarization can be regarded as a multi-document summarization in a specific field. Compared with the general multi-document summarization, the topic information can be characterized by some case elements that run through the entire text cluster. In text clusters, sentences and sentences are associated with each other, case elements also have associations of varying degree with sentences. These associations play an important role in extracting abstract sentences. A case-related public opinion summarization method based on graph convolution of sentence association graph with case elements is proposed, which uses graph structure to model all text clusters, with sentences as the main node, words and case elements as auxiliary nodes to enhance the relationship between sentences. Multiple features are used to calculate the relationship between different nodes. Then, graph convolutional neural network is used to learn this sentence association graph, and the sentence is classified to obtain the candidate summary sentence. Finally, the sentence is deduplicated and ranked to obtain the case-related public opinion summarization. Experiments are performed on the case-related public opinion summary dataset. The results show that the method achieves better results than the benchmark model, indicating that both the composition method and the graph convolution learning method are effective.