Identifying quartz, feldspar and cuttings in sandstone is of great significance for judging the dispositive environment, but traditional manual identification methods have problems of strong subjectivity and high dependence on experience. An image recognition method of sandstone micro-components based on Faster R-CNN targeted detection algorithm was constructed by using deep learning, convolutional neural network and other technologies. Intelligent recognition of three components, the average recognition accuracy of the three components is 89.28%. In order to verify the reliability of the model, the experiments compared different algorithms and feature extraction networks. The results show that the recognition effect of the Faster R-CNN targets detection algorithm is better than that of YOLOv3, YOLOv4, and YOLOv5s, and the performance of the ResNet50 features extraction network is better than VGG16. The Faster R-CNN targets detection model using the ResNet50 features extraction network has significant advantages, it can better meet the identification requirements of rock slices, and provide intelligent technical solutions to traditional manual methods.