In view of the problems of low efficiency, error propagation, and entity redundancy in traditional entities and relations annotation methods, and for the fact that there is the characteristic of "the overlapping relationship between one entity (main-entity) and multiple entities at the same time" in corpuses of some domains, a new annotation method for joint extraction of domain entities and relations was proposed. First, the main entity was marked as a fixed label, each other entity in the text that has relation with the main-entity was marked as the type of relation between the corresponding two entities. This way that entities and relations were simultaneously labeled was able to save at least half of the cost of annotation. Then, the triples were modeled directly instead of modeling entities and relations separately, and, the triple data were able to be obtained through label matching and mapping, which alleviated the problems of overlapping relation extraction, entity redundancy, and error propagation. Finally, the field of crop diseases and pests was taken as the example to conduct experiments, and the Bidirectional Encoder Representations from Transformers (BERT)-Bidirectional Long Short-Term Memory (BiLSTM)+Conditional Random Field (CRF) end-to-end model was tested the performance on the dataset of 1 619 crop diseases and pests articles. Experimental results show that this model has the F1 value 47.83 percentage points higher than the pipeline method based on the traditional annotation method+BERT model; compared with the joint learning method based on the new annotation method+BiLSTM+CRF model, Convolutional Neural Network (CNN)+BiLSTM+CRF or other classic models, the F1 value of the model increased by 9.55 percentage points and 10.22 percentage points respectively, which verify the effectiveness of the proposed annotation method and model.