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Mobile robots are easily affected by the surrounding environment when following moving targets, resulting in a decrease in target recognition accuracy and thus affecting the effectiveness of automatic following control. For this purpose, a deep learning based automatic target following control system for mobile robots was designed. The system framework is designed as a perception layer, processing and control layer, and execution layer. The visual sensor, ultrasonic sensor and MEMS sensor in the perception layer are used to collect information and transmit it to the processing and control layer. The MCU processor runs two programs. The former program uses the residual learning network, depth convolution network and Long short-term memory neural network in the depth learning to process images and identify targets. The latter program uses the ranging information of the ultrasonic sensor to calculate the target coordinates. The PLC microcontroller carries the control program, combines the angle information collected by MEMS sensors, and designs a dual loop controller based on PID to achieve automatic target following control of the mobile robot under its control. The experimental results show that, regardless of the environment, the number of misidentified and unrecognized images in the design system is relatively small, and the average error of angle following and following distance is relatively small. It has good target recognition function and strong ability to resist environmental interference, and is more suitable for target following in various environments, ensuring the effectiveness of automatic target following control for mobile robots.