Entity classification and relationship extraction in healthcare and medical field have attracted wide attention in recent years, and most of the work used the pipeline model in the past, which easily ignored the link between tasks and caused error propagation. Since joint learning can well avoid the two problems, a joint neural network model is established by combining the convolution neural network with support vector machine and conditional random field. On the basis of this model, entity classification and relationship extraction are studied jointly by task combination, model combination and feature combination in the way of parameter sharing. The joint neural network model achieves very good performance in the medicine instructions corpus, and the F-scores of entity classification and relationship extraction reach 98.0% and 98.3%, respectively. Experiments show that the joint neural network model is very effective for entity classification and relationship extraction in the medicine instructions corpus.