Construction of Intelligent Question-Answering System to Improve Knowledge Management Service from the Perspective of Education Informatization

With the continuous reform of education informatization, modern information technology gradually became a key technology in education. An intelligent question-answering system was constructed in this research for knowledge management services. It included an entity recognition model based on bidirectional long short-term memory attention conditional random field and a question classification model based on a bidirectional encoder using Transformers text convolutional neural network. The entity recognition model added a reverse long short-term memory propagation structure, while introducing an attention mechanism and conditional random field model. The question classification model utilized a preprocessed structure to output a feature matrix with richer semantic information, while utilizing a bidirectional encoder to extract local features of short text sentences. These experiments confirmed that the constructed model had a high precision of 0.945, which was much higher than other models. The recall of this proposed model was 0.077 higher than the control models. For precision, the proposed model had good recognition performance for methods, files and modules, with values of 0.9040, 0.9050 and 0.9160, respectively. For the recall, the question classification model had the best recognition effect on files, at 0.9060. For the [Formula: see text]1-score, the proposed model had the best recognition performance for modules, at 0.9100. The named entity recognition model constructed had high accuracy and [Formula: see text]1-score, reaching 0.931 and 0.923, indicating that it had the best classification performance in the dataset. Through intelligent question-answering systems, automatic answering of questions can be achieved, effectively improving knowledge services. This research has integrated knowledge and technology from multiple disciplines such as computer science, artificial intelligence, and natural language processing to promote the cross-integration of these disciplines in knowledge management. This interdisciplinary method not only broadens the research perspective of knowledge management but also provides new ideas and methods for the development of related fields.