In Chinese electronic medical records, the sentences are short and have the complex grammatical structure. In order to effectively recognize the medical entities, a novel named entity recognition based on multi-feature embedding and attention mechanism is proposed. After embedding three kinds of features derived from characters, words, and glyphs in the input presentation layer, the attention machine is introduced to the hidden layer of the bidirectional long short-term memory network, with the purpose of making the model focusing on the characters related to the medical entities. Finally, the optimal labels for five types of entities in Chinese electronic medical records, including diseases, body parts, symptoms, drugs, and operations are obtained. The experimental results for the open and self-built Chinese electronic medical records, the recognition accuracy, the recall rate and the F1 value of the proposed algorithm are all better than 97%, which shows that the proposed algorithm can more effectively identify various entities in Chinese electronic medical records.