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To allow fast and automatic detection of the same semantic agriculture-related questions, a method based on BERT-Attention-DenseGRU (gated recurrent unit) was proposed. According to the agriculture question characteristics, twelve layers of the Chinese BERT model method were applied to process and analyze the text data and compare it with the Word2Vec, Glove, and TF-IDF methods, effectively solving the problem of high dimension and sparse data in the agriculture-related text. Each network layer employed the connection information of features and all previous recursive layers’ hidden features. To alleviate the problem of feature vector size increasing due to dense splicing, an autoencoder was used after dense concatenation. The experimental results showed that agriculture-related question similarity matching based on BERT-Attention-DenseBiGRU can improve the utilization of text features, reduce the loss of features, and achieve fast and accurate similarity matching of the agriculture-related question dataset. The precision and F1 values of the proposed model were 97.2% and 97.6%. Compared with six other kinds of question similarity matching models, a state-of-the-art method with the agriculture-related question dataset was presented.