Abstract: There are many bottlenecks that limit the computing power of the mobile Web3D and they need to be solved before implementing a public fire evacuation system on this platform. In this study, we focus on three key problems: (1) The scene data for large-scale building information modeling (BIM) are huge, so it is difficult to transmit the data via the Internet and visualize them on the Web; (2) The raw fire dynamic simulator (FDS) smoke diffusion data are also very large, so it is extremely difficult to transmit the data via the Internet and visualize them on the Web; (3) A smart artificial intelligence fire evacuation app for the public should be accurate and real-time. To address these problems, the following solutions are proposed: (1) The large-scale scene model is made lightweight; (2) The amount of dynamic smoke is also made lightweight; (3) The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method. We propose a real-time fire evacuation system based on the ant colony optimization (RFES-ACO) algorithm with reused dynamic pheromones. Simulation results show that the public could use mobile Web3D devices to experience fire evacuation drills in real time smoothly. The real-time fire evacuation system (RFES) is efficient and the evacuation rate is better than those of the other two algorithms, i.e., the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.