Question and Answer Techniques for Financial Audits in Universities Based on Deep Learning

Financial auditing in universities is highly specialized, with a huge knowledge system and rapid updates. Auditors will encounter various problems and situations in their work and need to acquire domain knowledge efficiently and accurately to solve the difficulties they encounter. The existing audit information software, however, is mostly aimed at the management of audit affairs and lacks the relevant functions to acquire and retrieve knowledge of specific audit domains. In this study, we use deep learning theory as support to conduct an in-depth study on the key technologies of question and answer systems in the field of financial auditing in universities. In the question-answer retrieval stage, the local information and the global information of the sentence are first modelled using a two-way coding model based on the attentional mechanism, and then, an interactive text matching model is used to interact directly at the input layer, and a multilayer convolutional neural network model cable news network (CNN) is used to extract the fine-grained matching features from the interaction matrix; this study adopts two matching methods. We have conducted comparative experiments to verify the effectiveness and application value of the entity recognition algorithm based on this study’s algorithm and the question-answer retrieval model based on multi-granularity text matching in the university financial audit domain.