The positioning and tracking of sintering trolleys is the basis for high-quality process control of unsteady sintering, and it is also the key to the condition monitoring of trolleys. In view of the problem of material tracking and process control in the existing sintering production process, a new trolley positioning and tracking algorithm is proposed. The algorithm designs a convolutional neural network structure of complex number for the positioning scenario of sintering trolleys, and uses the depression angle of the complex number network to solve the problem of large positioning system error when the phase jumps from 360° to 0°. At the same time, GradCAM technology is used to analyze the working principle of the network to ensure the generalization ability of the model. In addition, the accuracy of the positioning information is confirmed twice by installing a positioning QR code on trolleys. After a year and a half of demonstration, it has been proved that the system has a high degree of reliability and stability, and can be effectively applied to material tracking and process control in the sintering production process.