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Agricultural named entity recognition is a fundamental tasks for natural language processing in the agricultural field. More importantly, it is the key basic step of constructing agricultural knowledge graph and intelligent question answering system. Traditional named entity recognition (NER) methods based on CRF model which relies on large amounts of hand-crafted features, cannot extract more effective features and solve the inconsistency of entity tagging caused by the diversity of entity names. To issue the above problems, an Att-BiLSTM-CRF framework was proposed based on deep learning. Firstly, the CBOW model was used to pre-train character embedding on a large number of unlabeled agricultural corpora, and alleviate the impact of segmentation accuracy on the performance of the model. Then, the document-level attention mechanism was introduced to obtain the similar information between entities in the text, so as to ensure the consistency of entity tagging in different contexts. Finally, based on BiLSTM-CRF benchmark model, a model framework suitable for agricultural named entity recognition was constructed. Totally 4604 agricultural texts were chosen to identify diseases, pests, pesticides and crop varieties. The experimental results showed that the model can effectively identify the entities in the agricultural text and alleviate the problem of inconsistent entity tagging. The model achieved good result in the agricultural corpus, and the recognition precision, recall, and F-score were respectively 93.48%, 90.60% and 92.01%. Compared with other models，such as LSTM model，LSTM-CRF model and BiLSTM-CRF model，Att-BiLSTM-CRF had obvious advantages in different size corpus, and it can effectively identify entities for agricultural texts.