Optimization of the Whole Process of Waste Classification: Construction of an Intelligent Monitoring System and Empirical Study on the Effect of Guiding Residents’ Participation Behavior

To address the insufficient whole-process coordination of waste classification in China, this paper constructs a three-tier intelligent monitoring system integrating textile recycling cycles into the “front-end, middle-end, and back-end” architecture and designs multidimensional guidance strategies for discarded fabric recovery based on the influencing factors of residents’ disposal behavior. Supported by wireless sensing and intelligent information acquisition technologies commonly employed in electromagnetic-enabled monitoring environments, the proposed framework optimizes textile resource regeneration through cognition improvement and incentive mechanisms while ensuring systematic management of garment waste alongside general household refuse. The collaborative effect is verified through a quasi-experimental method. Two old communities of similar scale are selected as research objects: the experimental group is equipped with the intelligent monitoring system and implements the guidance strategies, while the control group maintains the traditional model over a six-month experimental period. The results show that the participation rate of waste classification in the experimental group increases to 91%, the accuracy rate reaches 87%, the mixed transportation rate drops to 3%, the resource utilization rate rises by 18 percentage points, and the unit treatment cost decreases by 22.5%. The collaboration between the system and behavioral strategies significantly improves whole-process governance efficiency and demonstrates the value of integrating intelligent sensing, wireless data transmission, and environmental management. This study provides contextually adaptive and replicable technical solutions for waste classification while offering practical references for electromagnetic-assisted monitoring infrastructures and smart urban resource management.