This invention discloses an online education group question-and-answer matching method based on user style and time awareness, relating to the field of question-and-answer matching using
deep learning natural language processing technology. The method involves constructing a BigData dataset; dividing the BigData dataset into training, validation, and test sets; building user style-aware and time-aware question-and-answer matching models; training the models using the
training set and obtaining performance
metrics using the validation set to find the optimal hyperparameters; and inputting the
test set into the final user style-aware and time-aware question-and-answer matching model to obtain the matching results. This invention enhances question extraction by recognizing user style through user style awareness, reducing the
impact of
noise caused by severe imbalances between the number of questions and other types of dialogue. It also reduces the
noise caused by a large number of potential answers to a single question through time awareness. Compared with other traditional question-and-answer matching models, this method improves the model's question-and-answer matching performance and reduces the
impact of
data noise.