This paper proposes a design scheme for chest X-ray images analysis by using embedded technology and deep learning technology. The hardware platform of the analysis system using NIVIDIA′s Jetson TX2 as the core board, equipped with Ethernet modules, WiFi modules and other functional modules. It uses the MobileNets convolutional neural network on GPU server to train the marked chest X-ray image dataset then transplants the trained model to the Jetson TX2 core board, detecting the symptoms of pleural effusion, infiltration, emphysema, pneumothorax and atelectasis on the embedded platform. The chest X-ray image data provided by the National Institutes of Health(NIH) were tested in the trained model. Experiments have shown that this method gets higher accuracy and requires less time than other methods.