Objective To propose a drug word vector conversion model based on attention mechanism named Drug2vec for generating vectorized representation of drug information, and to compare the vector conversion effect with Word2vec and Med2vec. Methods Using the attention mechanism to capture the roles of medical entities on the central word, we proposed a Drug2vec model to convert medical entities in unstructured electronic medical records into vectors. Using the systemic lupus erythematosus (SLE) dataset of 14 219 patients and 963 drug entities, we tested the effect of the drug vectors generated by Drug2vec and compared it with the widely used language concept space vector conversion models Word2vec and Med2vec. Results In the SLE dataset, the accuracy of drug vectors generated by Drug2vec was higher than those of Word2vec and Med2vec models. The rank results of the similarity of drugs showed that the drug vectors generated by Drug2vec were consistent with the clinician's medication order. Conclusion Drug2vec model can more accurately modify central drug entities using contextual entities, producing more precise drug vectors.