The model based on encoder-decoder for joint extraction of entities and relations solve the error propagation problem of the pipeline model. However, the previous model based on encoder-decoder has two problems:the one is that entities and relations are generated in the decoding stage at the same time, so that the mapping of the same semantic space reduces the extraction performance because entities and relations are two different types, the other is that the interactive information between different relations is never considered. Aiming at these two problems, a relation-adaptive decoding model for joint extraction of entities and relations was proposed. In the proposed model, the joint extraction task of entities and relations was converted into the generation task of entity pairs corresponding relations. Firstly, based on encoder-decoder, different relations were divided and ruled, and based on different relations, the entity pairs corresponding to the relations were output adaptively, making the decoding stage focus on the generation of entities. Then, the parameters of one model were shared between different relations, so that the correlation information between different relations was able to be utilized. In the experiment, the proposed model had the F1 scores increased by 2.5 percentage points and 2.2 percentage points respectively compared to the state-of-the-art model on two versions of New York Times (NYT) public dataset. Experimental results show that the proposed model can effectively improve the joint extraction ability of entities and relations through the relation-adaptive decoding.