In order to solove the problem of slow detection speed and low recognition rate of common commodity recognition algorithms on the intelligent vending cabinet embedded system platform, an improved commodity recognition algorithm DS_YOLOv3 is proposed on the basis of YOLOv3. The traditional YOLOv3 neural network algorithm is improved by obtaining a prior bounding box suited for the image data of beverages sold in the vending cabinet by using k-means++ clustering algorithm, using the deep separable convolution to replace the standard convolution and adding the inverted residual module to reconstruct the YOLOv3 algorithm, which could reduce the computational complexity and enable real-time detection on the embedded platform, and introducing CIoU as the bounding box regression loss function to enhance the accuracy of target image positioning. The commodity testing experiment under typical scenarios is performed on a computer workstation and Jeston Xavier NX embedded platform. The results show that the accuracy of DS _YOLOv3 algorithm reaches 96. 73%, and the actual detection rate on the Jeston Xavier NX platform is 20. 34 fps, which meet the real-time and commodity detection recognition accuracy requirements of the intelligent vending cabinet based on the embedded system platform.