The work aims to add attention mechanism (Attention) and Joint Characteristics of Words in BiLSTM (Bidirectional Long Short-Term Memory) neural network to construct a BiLSTM deep learning model (Attention-BiLSTM) based on attention mechanism, so as to solve the problem of difficult identification of text-related entities in the packaging industry and recognize the packaging named entity. Firstly, the packaging domain dictionary was built to match with the category features of the words in the packaging corpus, and the packaging corpus was converted into the vector features of the word feature and the character feature, and then POS (part of speech) information was added in the process. The above features were then fed jointly to the BiLSTM network to obtain the global features of the text, and the attention mechanism was used to acquire the local features. Finally, the CRF (Conditional Random Field) was used to decode the optimal label sequence of the entire sentence according to the global features and local features of the text. The final F score was 85.6% on the "China Packaging Network" news dataset. The proposed method is superior to the traditional method in packaging named entity recognition.