This　paper　proposes　a　novel　model　for　named　entity　recognition　of　Chinese　crop　diseases　and　pests.　The　model　is　intended　to　solve　the　problems　of　uneven　entity　distribution,　incomplete　recognition　of　complex　terms,　and　unclear　entity　boundaries.　First,　a　robustly　optimized　BERT　pre-training　approach-whole　word　masking　(RoBERTa-wwm)　model　is　used　to　extract　diseases　and　pests＇　text　semantics,　acquiring　dynamic　word　vectors　to　solve　the　problem　of　incomplete　word　recognition.　Adversarial　training　is　then　introduced　to　address　unclear　boundaries　of　diseases　and　pest　entities　and　to　improve　the　generalization　ability　of　models　in　an　effective　manner.　The　context　features　are　obtained　by　the　bi-directional　gated　recurrent　unit　(BiGRU)　neural　network.　Finally,　the　optimal　tag　sequence　is　obtained　by　conditional　random　fields　(CRF)　decoding.　A　focal　loss　function　is　introduced　to　optimize　conditional　random　fields　(CRF)　and　thus　solve　the　problem　of　unbalanced　label　classification　in　the　sequence.　The　experimental　results　show　that　the　model＇s　precision,　recall,　and　F1　values　on　the　crop　diseases　and　pests　corpus　reached　89.23%,　90.90%,　and　90.04%,　respectively,　demonstrating　effectiveness　at　improving　the　accuracy　of　named　entity　recognition　for　Chinese　crop　diseases　and　pests.　The　named　entity　recognition　model　proposed　in　this　study　can　provide　a　high-quality　technical　basis　for　downstream　tasks　such　as　crop　diseases　and　pests　knowledge　graphs　and　question-answering　systems.