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Was designed and implemented a train wireless smoke alarm system based on semantic network, by using semantic network as a comprehensive information to describe the internal connection between the smoke properties, ARMS3C2440 as main control chip, semantic network nodes as a data exchange module, ZigBee wireless module of the overall train wireless smoke alarm system combined with the semantic network, accurate description of smoke concentration by using scalable semantic network distance threshold information characteristics, alarm, improve the alarm accuracy and timeliness. Setup smoke flow rate of 1.0 kg/s, the concentration is 2 kg/s, set the alarm valve value less than or equal to 25% of fire smoke concentration lower limit, and the overall gas fire smoke in the lower limit is 5%, so the smoke gas alarm upper limit is 1%. The result of simulation experiment shows that this kind of semantic network train wireless smoke alarm system can accurately analyzing the characteristic of smoke in the train, to timely and accurate warning of abnormal smoke, compared to the traditional alarm system, alarm accuracy increased by about 42%, and the alarm time reduce 2~3 s, have very strong application prospects.