YOLO algorithm loses part of the effective information of large-size feature maps in the downsampling process, which leads to the problem of insufficient target location in the detection task, which affects the overall detection accuracy of the model. This paper proposes the use of multi-scale feature fusion to solve the problem of inaccurate location of YOLO; First, modify the network model of YOLO algorithm, use different size feature maps in the YOLO network model with different feature attributes, and merge different size feature maps to improve the location accuracy of the detection network to the target; second, based on the pre-training model Re-train the modified network model on the last; finally, test the trained model in the computer. Experimental results show that the YOLO target detection algorithm based on multi-scale features improves the Accuracy rate by 3. 02% and improves mAP by 1. 53% compared with YOLO target detection algorithm.