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Traditional fire detection mechanisms which aims at using a single signal method results in misjudgements, complex wiring and low performance-to-price ratio. Aiming at solving these problems, a method of fire detection based on STM32F and extreme machine learning algorithms is proposed. For our model, the value of multiple sensors by STM32F module is collected, a WLAN is used as the carrier to transmit data and then denoised by weighted filter to obtain the training data for the ELM. After the model training, a simulation experiment on fire detection is finally carried out on a test data to evaluate and verify the resulting performance. The result shows that, our method can accurately identify fire types with 90% accuracy. In fire?signal?processing?algorithms, the proposed model is faster and achieves higher accuracy when compared with several state-of-the-art methods such as BP neural network, Naive Bayesian and SVM, and it is practical and worthy of using abroad.