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In the named entity recognition of apple diseases and pests, a entity recognition model was proposed to address the problems of insufficient semantic feature extraction for rare words and difficulties in distinguishing entities due to similar entity categories. This model integrated dynamic lexicon and convolutional block attention module (CBAM). Firstly, based on the bidirectional long short-term memory-conditional random field model (BiLSTM-CRF), a channel attention module (CAM) was used to dynamically obtain lexicon information for the words, and the fourcorner code information of Chinese characters was simultaneously fused to enhance the representation ability for rare words. Then after the sequence features output by the sequence encoding layer, a parallel connection spatial attention (PCSA) module based on the spatial attention module (SAM) was added to improve the model’s ability to extract contextual information. Finally, the model was validated and tested by using an apple disease and pest dataset which contained six major classes and 127574 annotated characters. The results showed that the precision, recall, and F1 value could reach 95.76%, 92.46% and 94.08%, respectively，indicating a significant improvement in performance compared with existing commonly used similar models, which achieved accurate recognition of agricultural disease and pest named entities.