Abstract: Aimed at the problem of gradient disappearance and network degradation caused by the use of recurrent neural networks in traditional question answering models, an automatic question answering model based on IMGRU-Seq2seq (Identity Mapping Gated Recurrent Unit-Sequence to Sequence) is proposed. The text was represented by weighted word vectors through the TF-IDF method. Based on the gated recurrent unit, the batch normalization technology and the rectified linear unit activation function were combined and the identity mapping was added to construct the IMGRU model. As the semantic extraction unit of the question answering model, the bidirectional IMGRU introduced the attention mechanism and the beam search algorithm to realize automatic question and answer. The experimental results show that the proposed method is 18.87% and 4.35% higher than the existing methods BLEU and ROUGE-L respectively.