To deal with the lack of word information in character vector embedding and the problem of Out-of-Vocabulary in Named Entity Recognition, an attention adaptive chinese named entity recognition (CNER) model based on vocabulary enhancement (ACVE) is proposed. The mechanism of potential information embedding is designed, which acquires word-level potential information by constructing semantic vectors, and the fusion embedding of character information and word-level information realizes the enhancement of semantic features; We also propose an attention mechanism for adaptive distribution, which adaptively adjusts the position of attention by introducing a dynamic scaling factor to obtain the attention distribution suitable for NER tasks. Experiments on a special field dataset with a large number of out-of-vocabulary (OOV) words show that, compared with state-of-the-art methods, our method is more effective and achieves better results.