Abstract: In view of the existing problems of strong lag and low accuracy of fault identification in the original early warning system for gas extraction pipelines, a type of fault early warning system for gas extraction pipelines based on multi-parameter fusion perception and intelligent algorithm is designed. This system collects pipeline operating status parameters in real time by deploying gas concentration, pressure, flow rate, vibration and temperature sensors. After being pre-processed by edge computing nodes, they are transmitted to the monitoring center through LoRa wireless communication, and fault type identification and early warning are achieved by utilizing the improved random forest algorithm. The test results show that the fault early warning system has an accuracy rate of 97.2% in identifying typical faults, with an average response time of ≤ 1.5 s, which can adapt to downhole high humidity, high dust, and strong electromagnetic interference environments, providing technical support for the safe and stable operation of gas extraction systems.