Target recognition has been the hot issue in the field of artificial intelligence. In order to enhance the efficiency of target recognition, this paper proposes a method based on multilayer feature extraction of convolutional neural network. By inputting images into convolutional neural network for training, this method implements feature extraction at each full connection layer of network, inputs the features obtained into classifier, and then compares the output results. The lower full connection layer activated by relu function is selected as feature extraction layer, whose recognition rate is higher than that in higher full connection layer. This paper builds up office supplies dataset, and realizes the office supplies identification system based on the multilayer feature extraction of convolutional neural network. The layer relu6 of AlexNet is selected feature extraction layer, and the optimal training image quantity as well as the optimal classifier construction system is chosen, which verifies the feasibility of this method.