Aiming at the problems of lack of potential implicit relation mining between entities and insufficient head entity information extraction in existing methods, a head entityenhanced multihop attention implicit relations joint mining model MultiAir (multihop attention implicit relations joint mining method) was proposed. The method first uses the BERT (bidirectional encoder representations from transformers) model to encode the features of the input sentence and predicts the position of the head entity through the Sigmoid function, and then uses the bidirectional gated recurrent unit (BiGRU) to enhance the feature of the head entity. After making full use of the highlevel information of the head entities, the model can output the possible starting and ending positions of the tail entities with multiple relationships. Then the model continues to use the tail entities as the head entities of the next hop and iteratively perform the prediction of the multihop tail entities. At the same time, the model uses the attention weight to dynamically adjust the features of the learned entities and relationships so as to realize the mining of potential relationship triples in the plain text. The MultiAir model has made good improvements in both the public dataset NYT and the civil aviation emergency dataset.