Corn is one of the main grain crops in China and its production accounts for more than 20% of the World’s corn production. Weed is one of the most important factors influencing maize yield. Effective recognition method of cron and weed can improve corn quality and production accounts. At present, pesticide spraying is the main way of removing weed in China. Excessive spraying of pesticides brings problems such as environmental pollution and food safety, and therefore precise spraying is the key of weeding to reduce the amount of pesticides and increase the utilization of pesticides. Precise application of pesticides is based on accurate identification of weeds, researchers at home and abroad have done a lot of research. Most existing weed identification methods rely on manually selected weed features, such as shape, texture, etc., which takes longer time to identify the image, and the accuracy of identification still needs further improvement. The deep learning method was used to achieve automatic extraction of weed image features without relying on artificial feature screening, and combined the binary Hash code to compress high-dimensional weed feature data to achieve rapid weed identification and provide information support for subsequent field drug spraying. In order to improve accuracy of crop and weed identification, combining with the strong feature extraction capabilities of the deep convolutional network and the ease of storage and fast retrieval of the Hash code, a fast field weed identification method was proposed based on the deep convolutional network and binary Hash code. A pretrained multilayer convolutional neural network was used to construct a weed identification model with a binary Hash layer, and the model was fine-tuned with the collected weed data set. The binary Hash layer proposed could effectively compress the highdimensional weed image features to facilitate the storage and subsequent calculation of the highdimensional weed image features. During tests of weed identification, the trained model was used to extract the fullconnection layer feature codes and binary Hash codes of the input image, and then compared with the fullconnection layer feature codes and binary Hash codes stored in the database to calculate the Hamming distance and the Euclidean distance. After that, the most similar K images could be found out according to last step’s results. Finally, the labels’ frequency of the K images was counted and the original image was classified into the highest frequency category of label to achieve the purpose of weed identification. The effects of different layers of convolutional networks and different length binary Hash code on weed identification were compared, and finally the weed identification model was determined, which included four layers convolutional neural network and 128bit binary Hash code. The experimental results showed that the method proposed could achieve 98.6% accuracy in field weed identification, and the loss function stability was improved compared with the ordinary model. At the same time, it also performed well on other weeds datasets with an accuracy of 95.8%, which meant that the proposed method was universal. The research results could provide reference for precision weeding. The experiment carried out in corn field showed that the method could achieve 92.7% accuracy, and it could effectively reduce pesticide waste which was suitable for precision spray.