Abstract: The traditional soybean database has a narrow range of knowledge coverage and complicated invalid information,which makes it impossible for soybean growers to effectively solve production problems in the Internet.Knowledge graphs provide a way to extract knowledge from massive text and image data,enabling users to quickly and effectively retrieve information.Therefore,this paper firstly constructs a soybean planting management knowledge graph based on existing open information,and builds a related question and answer system to help soybean growers solve problems encountered in the planting process.Specifically,the paper uses a top-down knowledge graph construction method to collect existing knowledge and prior knowledge in professional fields,and uses BIO method to label data.Then,it constructs the knowledge graph after extracting entities through Bert-BiLSTM-CRF model.Finally,by using the Bert-BiLSTM-CRF model and the Bert+TextCNN model,it completes the named entity recognition task and the user intent judgment task to build the question and answer system.Experimental results show that the soybean planting management knowledge question and answer system constructed in this paper can effectively answer problems encountered in the planting process,which proves that the question and answer system can be applied in the practice.

Key words: Named entity recognition, Intent inference, Knowledge graph, Planting management, Smart agriculture