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The current research on the sorting robot fault detection system has low detection accuracy, resulting in large errors in detection results and poor real-time performance. To this end, a new sorting robot fault detection system is designed based on the Internet of Things, and the hardware and software of the system are designed separately. Select the pulley type robot carrier to set the sorting robot, the hardware part uses Zigbee pressure sensor to complete the robot fault information collection, the XBEE module is used for data transmission, and the sorting central control machine is coordinated to receive the information collected by each sensor. The collected data is sent to the upper computer to realize the controller design. The software workflow is realized through information calibration, information collection, feature extraction, and fault identification. The non-maximum maximum between-class variance method is used to screen out the optimal high and low threshold solutions, and the continuous but false edges of the fault information image edges are obtained. Map the extracted image feature vector to the type space, obtain the recognition classification result, determine the cause of the fault, and complete the fault recognition. The experimental results show that the sorting robot fault detection system based on the Internet of Things can effectively improve the detection accuracy and strengthen the real-time performance of the detection results.