Traditional convolutional neural networks have a large demand of computation and memory,which makes the development of embedded devices for intelligent applications become a challenge.In order to deploy highly complex deep learning applications into the low-cost embedded platforms with limited performance,a small embedded system for image classification is designed.Based on the Structured Sparsity Learning (SSL) algorithm,a sparse convolutional neural network model is constructed under the framework of Caffe and deployed on IndustriPi minimization system.Test results show that the accuracy of85.5% and operating speed of more than 8 frames per second are achieved.Compared with classical models,the sparse model can reduce computational amount and memory occupancy to a great extent,and increase the embedded device operating speed.