Interactive learning system neural network algorithm optimization.

Abstract

With the development of artificial intelligence education, the human-computer interaction and human-human interaction in virtual learning communities such as Zhihu and Quora have become research hotspots. This study has optimized the research dimensions of the virtual learning system in colleges and universities based on neural network algorithms and the value of digital intelligence in the humanities. This study aims to improve the efficiency and interactive quality of students' online learning by optimizing the interactive system of virtual learning communities in colleges. Constructed an algorithmic model for a long short-term memory (LSTM) network based on the concept of digital humanities integration. The model uses attention mechanism to improve its ability to comprehend and process question-and-answer (Q&A) content. In addition, student satisfaction with its use was investigated. The Siamese LSTM model with the attention mechanism outperforms other methods when using Word2Vec for embedding and Manhattan distance as a similarity function. The performance of the Siamese LSTM model with the introduction of the attention mechanism improves by 9%. In the evaluation of duplicate question detection on the Quora dataset, our model outperformed the previously established high-performing models, achieving an accuracy of 91.6%. Students expressed greater satisfaction with the updated interactive platform. The model in this study is more suitable than other published models for processing the SemEval Task 1 dataset. Our Q&A system, which implements simple information extraction and a natural language understanding method to answer questions, is highly rated by students.