Aiming at the problem of insufficient accuracy and timeliness of wildfire monitoring, a near real-time fire monitoring algorithm using a Multichannel Convolutional Neural Network (MCNN) with a cascaded traditional method is proposed. Firstly, by combining the OTSU method and the spatial context method, potential fire points are identified by exploiting the differences in background brightness temperature spatial information. Secondly, using the idea of ensemble learning, three convolutional neural network channels are constructed. Each channel takes different combinations of spectral information, spatial context information, and temporal-geographical information features as input. The optimal weights for each channel are obtained by using the particle swarm optimization algorithm to search for the best weights, and the joint prediction probabilities of fire points from the three channels are obtained, achieving accurate fire point recognition. The results show that compared to a single-channel Convolutional Neural Network (CNN) model, the MCNN achieves a precision of 0.88 and reduces the omission rate by 0.16. Furthermore, compared to the Japan Meteorological Agency’s official product, the omission rate is reduced by 0.06. In addition, the highest runtime of the model in the experiment is 268 seconds. Therefore, the MCNN model proposed in this paper can achieve high-precision near real-time fire point detection, providing a scientific basis for emergency fire response.