Abstract: 【Objective】 Aiming at the problems of few crop pest dataset samples, low accuracy of existing single model in crop pest identification and poor generalization ability, a pest identification model based on transfer learning and multi-model integration is proposed. 【Methods】 Experiments were carried out on the large-scale public crop pest dataset IP102. In this study, transfer learning is used to train 6 deep neural networks separately, and the combination of EfficientNet, Vision Transformer, Swin Transformer and ConvNeXt with better recognition performance was selected, and then different strategies were used to integrate the prediction results. 【Results】 The results showed that the recognition accuracy of the proposed method based on transfer learning and multi-model integration reached 75.75%, which was 1.34% higher than that of the best-performing single-model ConvNeXt, and was comparable to the current compared with the performance of the optimal algorithm (CA-EfficientNet) on the dataset, the recognition accuracy was 6.3% higher, 【Conclusion】 It has better stability and generalization ability.