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Named entity recognition of wheat diseases and pests is a key step to building a knowledge graph. Aiming at the problems of lack of training data, complex entity structure, diverse entity types and uneven entity distribution in wheat diseases and pests field, under the promise of fully mining the implicit knowledge, two data augmentation methods are used to expand sentence semantic information, and to construct the corpus WpdCNER (wheat pests and diseases Chinese named entity recognition) and the field lexicon WpdDict (wheat pests and diseases dictionary). And 16 categories of entities are defined with the field experts’ guidance. Meanwhile, Chinese named entity recognition model based on rules amendment WPD-RA (wheat pests and disease-rules amendment model) is proposed. This model is carried out entity recognition based on ALBERT+BiLSTM+CRF (a lite bi-directional encoder representation from transformer + bi-directional long short-term memory + conditional random field), and specific rules are defined to amend entity boundaries after recognition. The WPD-RA model achieves the best results with 94.72% precision, 95.23% recall, and 94.97% F1. Its precision is increased by 1.71 percentage points, recall is increased by 0.34 percentage points, and F1 is increased by 1.03 percentage points, compared with the model without rules. Experimental results show that the model can effectively recognize named entities in wheat diseases and pests field, and its performance is better than other models. The proposed model provides a reference idea for named entity recognition task in other fields such as food safety and biology.