The electronic nose system can identify and detect the toxic gases released by flammable materials during combustion. This paper studies the application of the e-nose technology in the early fire detection of flammable materials. Based on the brief introduction of the e-nose system and the associated pattern recognition technology, this paper builds a high-throughput flammable material fire simulation platform, and then, with the PVC material of the wires and cables in the electrical equipment which are prone to fire as an example, it detects the volatile substances within the safe operating temperature range (100-180°C) and at the warning temperature point (200°C) during the combustion process. In order to verify the anti-interference ability of the platform, this paper selects liquor and cigarette as interferences, which are also subject to detection in the experiment. It uses discriminant function analysis (DFA) and the BP neural network method to perform statistical analysis of the collected data and the results show that both methods have good discriminant effects. At the same time, it also optimizes the sensor array by the load analysis method. Through comparison and analysis, it is found that the eight-sensor array has a better discriminant effect. The research results show that the electronic nose technology can realize the early detection of flammable materials.