Aiming at the problem that the classification and detection of colorectal polyps by common computeraided detection systems are not accurate and realtime, a YOLFCBAM model combined with spatial attention mechanism (CBAM) and improved feature fusion layer based on YOLOv4 is proposed, which can classify and detect hyperplastic polyps and adenomatous polyps in dual modal of white light and NBI endoscopic images in real time. In order to make the feature extraction of polyps more accurate, a CBAM module is integrated to the backbone of YOLOv4, so that the network feature extraction layer pays attention to more important spatial and channel information, and inhibits the downward transmission of unnecessary features. On this basis, the network structure is optimized by pruning the feature fusion layer PANet to reduce the amount of network parameters and further improve the detection speed of the model. In order to train and test the improved model, 2 988 white light and NBI endoscopic images are collected from the Affiliated Hospital of Hebei University, and are divided into training set and test set at a ratio of 9∶1. Experimental results show that our proposed YOLOFCBAM achieves a mAP of 8644%, recalls of 8962% and 8564% for identifying hyperplastic and adenomatous polyps respectively, accuracies of 9135% and 8519% for identifying hyperplastic and adenomatous polyps respectively, and a classification speed of 47 FPS on the test set, which proves that the proposed model has potential clinical application value.