With the rapid development of the new energy vehicle industry, its unique electrical fire hazards have become increasingly prominent. To address this issue, this paper constructs a new energy vehicle electrical fire risk monitoring platform based on multi-source data fusion technology. By integrating information from various aspects such as battery status, charging parameters, and environmental indicators, and combining artificial intelligence analysis methods, the platform achieves early identification and intelligent alarm of electrical fire risks. The platform adopts a hierarchical architecture, including the perception layer, network layer, data processing layer, and application layer, supporting a full closed-loop risk prevention and control process starting from data collection. A 3-month pilot application at a dedicated
new energy vehicle charging station in Beijing showed that the platform increased the fire early warning accuracy from 72.3% (with traditional single-parameter monitoring) to 98.5%, shortened the average response time from 15 seconds to 2 seconds, and controlled the false alarm rate below 1%. It significantly improved the accuracy of fire prediction and response speed, providing technical support for the safe operation of new energy vehicles.