This study aims to design a GIS online monitoring and fault intelligent diagnosis system based on the joint analysis of vibration and sound. The system integrates vibration and acoustic signal acquisition technology and utilizes ant colony algorithm optimized support vector machine for fault diagnosis. Through experiments conducted on 220 kV GIS, the system has been validated to rapidly and accurately identify vibration and acoustic signals under different fault conditions, providing reliable early warning and diagnosis. This system overcomes the limitations of traditional single physical quantity detection, enhances the sensitivity and reliability of online monitoring for GIS equipment, and offers an intelligent solution for power system safety management. The research findings provide an intelligent and efficient monitoring and management solution for GIS equipment in the power industry, with the potential to improve the safety and reliability of power equipment in practical applications. In the future, further optimization of algorithms and functionalities can be pursued to meet the evolving monitoring needs of power systems.