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In order to solve the problems of low recognition rate and poor robustness of cotton seedlings and cross-growth of various weeds distribution status, seven kinds of common weeds in the field were taken as the research object under natural conditions of Xinjiang cotton seedling period. A Faster R-CNN method of growing cotton seedling weed identification with data augmentation was proposed. A total of 4694 images of weeds in cotton seedling stage under different growing backgrounds and different weather conditions were collected, then the objects of images were annotated and the data sets were augmented. The suitable anchor scale of the model was designed, and four feature extractors involving VGG16, VGG19, ResNet50 and ResNet101 were compared. VGG16 was selected as the optimal feature extractor to train cotton seedling and weeds images and optimized Faster R-CNN network detection model was obtained for weeds of different weather conditions and the variety growth status, which can effectively identify and localize seven types of weeds and cotton seedlings. The average identification time for single picture was 0.261s and the average precision of optimized Faster R-CNN was 94.21%. With the same sample, characteristic extractor network, computer condition, the proposed method was compared with the state-of-the-art methods YOLO and SSD algorithms. The results showed that the proposed Faster R-CNN model had obvious advantages in the identification of various weeds in the seedling stage of cotton field. The trained model was placed in field environment for verification test. During the recognition process, totally 150 valid images were verified, and the average recognition rate reached 88.67%. The average recognition time for each image was 0.385s. The result indicated that the proposed method had certain applicability and generalization in precise control of weeds.