Aiming at the robustness of rape pest identification methods in terms of background, angle, posture and illumination, a method was proposed based on deep convolutional neural network to detect rape pests. Firstly, the detection model of rape pest was constructed on the basis of convolutional neural network and region proposal network. Secondly, the model was tested on the deep learning tensor flow framework. Finally, the experimental results were compared and analyzed. Based on convolutional neural networks and regional candidate networks, a rape pest detection model was constructed by using the VGG16 network to extract the features of the rape pest image, the region proposal network to generate the preliminary position candidate box of the rape pest, and the Fast R-CNN to realize the classification and localization of the candidate box. The results showed that the method can quickly and accurately detect five species of rape pests such as aphids, caterpillars (larvae), dish, jumping and leaf, with the average accuracy rate of 94.12%. Compared with the RCNN, Fast R-CNN, multi-feature fusion method and color feature extraction method, the accuracy rate of new method improved 28%, 23%, 12%, and 2%, respectively.