Recognizing entity information in clothing quality sampling notice is important for assessing the quality status of clothes in different regions as well as formulating macro policies. Aiming at the problems of loss of information for long text sequences, and incomplete feature learning of small class samples in named entity recognition for clothing quality sampling notice. With the focus on the attention mechanism, a domain named entity recognition model based on the BERT (bidirectional encoder representations from transformers) and TENER (transformer encoder for NER) model was proposed. The dynamic character vectors of characters were obtained by the pre-training model BERT. These character vectors were input into the TENER module, which made the same characters undergo different learning processes based on the attention mechanism. The distance and direction information between characters were further captured, enhancing the model's understanding of the text content of different lengths and small categories. The conditional random field model was used to obtain the entity label corresponding to each character. On the domain dataset, the entity recognition F₁ of the BERT-TENER model for the clothing sampling domain reaches 92.45%. This model has not demonstrated applicability in other areas. The model effectively improves the named entity recognition rate compared with the traditional methods, and also shows better performance in long text as well as unbalanced entity categories.