###### Abstract  
It is always a hot issue in the intelligence analysis field to predict the trend of news description by pre-trained language models and graph neural networks. However, there are several problems in the existing research: (1) there are few Chinese data sets on this subject in academia and industry; and (2) using the existing pre-trained language models and graph classification algorithms cannot achieve satisfactory results. The method described in this paper can better solve these problems. (1) We built a Chinese news database predicted by more than 9000 annotated news time trends, filling the gaps in this database. (2) We designed an improved method based on the pre-trained language model and graph neural networks pooling algorithm. In the graph pooling algorithm, the Graph U-Nets Pooling method and self-attention are combined, which can better solve the analysis of the problem of forecasting the development trend of news events. The experimental results show that the effect of this method compared with the baseline graph classification algorithm is improved, and it also solves the shortcomings of the pre-trained language model that cannot handle very long texts. Therefore, it can be concluded that our research has strong processing capabilities for analyzing and predicting the development trend of Chinese news events. natural language processing; graph convolution neural network (GCN); graph pooling algorithm +
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0000-0001-9900-0001-9900-0001-9900-0001-9900-0001-99001-90001-99001-99001-99001-99001-99001-99001-99001-99001-99001-9001-99001-99001-99001-99001-9001-99001-99001-9001-99001-9001-9001-9001-9001-9001-9001-9001-900 same road section and different time background [2], node-level task three-dimensional structure component assembly technology [3], and so on. In the field of natural language processing, graph convolutional neural networks also have many creative applications. For example, graph convolutional networks can be used to analyze user speeches in social networks and build user portraits. On shopping sites, graph convolutional networks can be used to analyze user preferences and predict the products that users want to buy while recommending [4]. For traditional text classification tasks, Text GCN using knowledge graphs and graph convolutional neural networks has made great progress in this regard [5]. The author constructs a graph on the entire corpus, takes words and entities as nodes in the graph, uses co-occurrence relationship information to construct edges in the graph, and then regards the text classification problem as a node classification problem. At the same time, in the past two years, researchers have carried out a variety of studies following the path of Text GCN. With the development of pre-trained language models in the past few years, researchers have increasingly used pre-trained language models to implement text-related tasks. Compared with the previous language models, the pre-trained language model effectively learns global semantic information and significantly improves the accuracy of natural language processing tasks. Among them, representative pre-trained language models such as ELMo [6], GPT [7; 8; 9], and BERT [10] have further improved the effect of pre-trained language models. ELMo uses a bidirectional LSTM model, which consists of one forward and one backward language model. The objective function takes the maximum likelihood of these two directions and summates each middle layer of the bidirectional language model. Later, when performing supervised NLP tasks, ELMo can be directly used as features to be spliced to the word vector input of the specific task model or the highest-level representation of the model. After that, the OpenAI released the GPT (Generative Pre-Training) language model in order to build an unsupervised general language model in a variety of natural language processing tasks.