Aiming at the problems of traditional guide devices such as single environmental perception and poor terrain adaptability, this paper proposes an intelligent guide system based on a quadruped robot platform. Data fusion between millimeter-wave radar (with an accuracy of ± 0.1°) and an RGB-D camera is achieved through multi-sensor spatiotemporal registration technology, and a dataset suitable for guide dog robots is constructed. For the application scenario of edge-end guide dog robots, a lightweight CA-YOLOv11 target detection model integrated with an attention mechanism is innovatively adopted, achieving a comprehensive recognition accuracy of 95.8% in complex scenarios, which is 2.2% higher than that of the benchmark YOLOv11 network. The system supports navigation on complex terrains such as stairs (25 cm steps) and slopes (35° gradient), and the response time to sudden disturbances is shortened to 100 ms. Actual tests show that the navigation success rate reaches 95% in eight types of scenarios, the user satisfaction score is 4.8/5.0, and the cost is 50% lower than that of traditional guide dogs.