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In order to improve the accuracy and stability of the motion pose of the wall-climbing robot and solve the problem of poor control effect of the existing pose positioning control system, the big data clustering analysis technology is used to realize the wall-climbing through the design of software and hardware structure. Optimization of robot pose positioning control system. Modify the internal connection structure and working method of the wall-climbing robot pose sensor, the wall-climbing robot driving element, and the wall-climbing robot pose positioning controller, expand the storage space of the system memory, and complete the hardware system design through the connection of the system circuit. According to the composition structure and working mechanism of the wall-climbing robot, the mechanical structure and motion model are established. Under the construction of the model, the real-time operation data of the wall-climbing robot is collected, and the big data cluster analysis technology is used to process the initial collected data to determine the current pose of the wall-climbing robot. Plan the pose and joint trajectories of the wall-climbing robot, and combine the current running data of the wall-climbing robot to calculate the position and orientation control amount of the wall-climbing robot. Through the system test experiment, it is concluded that by designing the application of the pose positioning control system, the foot trajectory control error, joint angle control error and duty cycle control error of the wall-climbing robot prototype are all lower than the preset values, that is, the designed system has Good pose positioning control effect.