In order to realize rapid and accurate identification of rice pests, a rice pest identification method based on transfer learning and convolutional neural network was proposed in this paper. First, the images of rice pests were preprocessed. Pre-processing methods include translation, inversion, rotation, and scaling. According to the characteristics of the pests, the images were divided into six categories, namely, rice leaf roller, rice planthopper, rice plant thopper, rice leaf roller, rice plant thopper, rice plant thopper, rice locust, and rice weevil. Then, based on the transfer learning method, the weight parameters trained by the VGG16 model on the image data set ImageNet were transferred to the recognition of rice pests. The convolution layer and the pooling layer of VGG16 were used as the feature extraction layer. Meanwhile, the top layer was redesigned as the global average pooling layer and a softmax output layer. Part of the convolutional layer is frozen during training. The experimental results show that the average test accuracy of this model is 99.05%, the training time is about 1/2 of the original model, and the model size is only 74.2 MB. The F1 values of six insect pests, namely, rice grasshopper, rice planthopper, rice weevil, striped rice borer, the rice leaf roller, and yellow rice borer, were 0.898, 0.99, 0.99, 0.99, 1.00, 0.99, respectively. The experimental results show that this method has high identification efficiency, good identification effect and strong portability, which can provide a reference for the efficient and rapid diagnosis of crop pests.