This paper presents an improved faster R-CNN algorithm based on the application of unmanned vending machine selling bottled drinks. Firstly, the residual network ResNet-50 is used as the feature extraction network to deepen the depth of target feature extraction and learning. Then, the number and style of anchor frame in regional proposal network (RPN) is improved according to the morphological characteristics of bottled beverage products. Finally, a multi-dimensional feature map fusion network is proposed to enhance the detection performance of small targets. The experimental results showed that, the loss value tends to converge after 10 000 iterations of model training. Average precision values of 10 categories of bottled beverage products are all larger than 90%. And the comprehensive detection recognition rate mean average precision value is 93.26%, which is improved 20% compared with the original model.