Abstract: The modern logistics and intelligent manufacturing fields have higher requirements for the efficiency and accuracy of material sorting, and the traditional manual sorting mode is difficult to adapt to the needs of large-scale production. Building an automatic sorting system that integrates material perception, signal processing, logical decision-making, and execution driving, with sensor detection and programmable logic controller collaborative control as the core, can achieve rapid identification and accurate classification of material attributes. The system integrates multiple types of sensors such as optoelectronics, color, and weight, and is equipped with Siemens S7 series PLC to complete the hardware architecture construction. The sorting stability is improved through image processing and control algorithm optimization. Based on the integrated teaching needs of vocational education theory and practice, the system will be modularized and transformed into a teaching platform that balances engineering applications and skill development. The renovated system can support core skills training such as sensor selection, PLC programming, and electromechanical integration, effectively linking industry technical standards and teaching content, and improving the quality of automation professional talent training.