Along with the explosion of ChatGPT, the artificial intelligence question-answering system has been pushed to a climax. Intelligent question-answering enables computers to simulate people’s behavior habits of understanding a corpus through machine learning, so as to answer questions in professional fields. How to obtain more accurate answers to personalized questions in professional fields is the core content of intelligent question-answering research. As one of the key technologies of intelligent question-answering, the accuracy of text matching is related to the development of the intelligent question-answering community. Aiming to solve the problem of polysemy of text, the Enhanced Representation through Knowledge Integration (ERNIE) model is used to obtain the word vector representation of text, which makes up for the lack of prior knowledge in the traditional word vector representation model. Additionally, there are also problems of homophones and polyphones in Chinese, so this paper introduces the phonetic character sequence of the text to distinguish them. In addition, aiming at the problem that there are many proper nouns in the insurance field that are difficult to identify, after conventional part-of-speech tagging, proper nouns are distinguished by especially defining their parts of speech. After the above three types of text-based semantic feature extensions, this paper also uses the Bi-directional Long Short-Term Memory (BiLSTM) and TextCNN models to extract the global features and local features of the text, respectively. It can obtain the feature representation of the text more comprehensively. Thus, the text matching model integrating BiLSTM and TextCNN fusing Multi-Feature (namely MFBT) is proposed for the insurance question-answering community. The MFBT model aims to solve the problems that affect the answer selection in the insurance question-answering community, such as proper nouns, nonstandard sentences and sparse features. Taking the question-and-answer data of the insurance library as the sample, the MFBT text-matching model is compared and evaluated with other models. The experimental results show that the MFBT text-matching model has higher evaluation index values, including accuracy, recall and F1, than other models. The model trained by historical search data can better help users in the insurance question-and-answer community obtain the answers they need and improve their satisfaction.

With the rapid development of social network platforms, Sina Weibo has become the main carrier for modern netizens to express public views and emotions. How to obtain the tendency of public opinion and analyze the text’s emotion more accurately and reasonably has become one of the main challenges for the government to monitor public opinion in the future. Due to the sparseness of Weibo text data and the complex semantics of Chinese, this paper proposes an emotion analysis model based on the Bidirectional Encoder Representation from Transformers pre-training model (BERT), Fast Gradient Method (FGM) and the bidirectional Gated Recurrent Unit (BiGRU), namely BERT-FGM-BiGRU model. Aiming to solve the problem of text polysemy and improve the extraction effect and classification ability of text features, this paper adopts the BERT pre-training model for word vector representation and BiGRU for text feature extraction. In order to improve the generalization ability of the model, this paper uses the FGM adversarial training algorithm to perturb the data. Therefore, a BERT-FGM-BiGRU model is constructed with the goal of sentiment analysis. This paper takes the Chinese text data from the Sina Weibo platform during COVID-19 as the research object. By comparing the BERT-FGM-BiGRU model with the traditional model, and combining the temporal and spatial characteristics, it further studies the changing trend of user sentiment. Finally, the results show that the BERT-FGM-BiGRU model has the best classification effect and the highest accuracy compared with other models, which provides a scientific method for government departments to supervise public opinion. Based on the classification results of this model and combined with the temporal and spatial characteristics, it can be found that public sentiment is spatially closely related to the severity of the pandemic. Due to the imbalance of information sources, the public showed negative emotions of fear and worry in the early and middle stages, while in the later stage, the public sentiment gradually changed from negative to positive and hopeful with the improvement of the epidemic situation.