With the continuous and rapid development of Artificial Intelligence (AI), machine vision and embedded control have progressively become foundational technologies for the intelligent manufacturing industry. To meet the urgent demand for teaching and experimental platforms amidst the reform of AI education in universities, this paper proposes and implements an intelligent sorting system based on the Robot Operating System 2 (ROS2) framework. The system utilizes a Raspberry Pi as the upper-computer platform for vision acquisition and inference. Real-time video streams are collected via a USB camera, and OpenCV is employed for preprocessing operations, including video frame decoding, color space conversion, and scaling. Subsequently, ONNX Runtime is utilized for the deployment and inference of deep learning models. At the execution level, the system employs an ESP32 microcontroller as the ROS2 lower-level node. It establishes stable communication with the Raspberry Pi over a Local Area Network (LAN) via micro-ROS, enabling precise control of the conveyor belt motor and the pusher mechanism. All nodes operate within the same Wireless LAN (WLAN) and utilize the DDS protocol for rapid node discovery and reliable message transmission. This paper provides a detailed introduction to the system's design, covering hardware structure, vision processing workflows, communication architecture, and actuator control. Furthermore, the stability, real-time performance, and scalability of the system are validated through multiple rounds of experiments. Finally, centering on the requirements of educational platform construction, this paper analyzes the value of the system in experimental teaching and discusses its future application prospects in intelligent manufacturing and university laboratory platforms.