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Bursaphelenchus is the key quarantine object of the customs quarantine department, but there are many kinds of Bursaphelenchus and their morphological changes are very complicated. Therefore, the identification of Bursaphelenchus has long been a difficult problem in the quarantine work of various customs. We propose a new identification method based on convolutional neural network with pluggable attention module. The method first uses the convolutional neural network to extract the features of Bursaphelenchus, and then obtains the attention map of the characteristic part of Bursaphelenchus through the pluggable attention module, adds the attention map to the identification network for training, and then strengthens the attention of different region weights, thereby strengthening the neural network to focus on learning the key feature regions of nematodes, so that the network can more accurately identify effective feature information. Compared with the traditional neural network model, the algorithm model proposed in this paper has achieved a high recognition accuracy rate and can effectively identify samples that are difficult to distinguish. The algorithm module proposed in this paper has been able to applied to the identification and classification of Bursaphelenchus in the quarantine department of Ningbo Customs.