The paper introduces the method of combining word vector and word attribute reasoning to realize Named Entity Recognition (NER) of Chinese Electronic Medical Record (EMR) based on BiLSTM-CRF benchmark model, including medical corpus construction, word vector training, integration of word attribute reasoning, etc., analyzes the experimental results, and points out that the introduction of word vector and word attribute reasoning in medical field can improve NER effect of Chinese EMR.