The traditional fire smoke detection method has a degraded detection performance in the case of complex scenes and high interference. Aiming at this problem, this paper proposes a convolutional neural network fire smoke detection method based on target region. A two-layer fire smoke detection model is constructed. Using the motion detection algorithm of the target region positioning layer, the smoke target region is extracted from the fire smoke image, which can quickly remove a large amount of irrelevant interference information in complex scenes, and input the extracted smoke target region into the fire smoke recognition layer, and then extract the deep features of the smoke through the convolutional neural network to classify it to complete the fire smoke detection. Experimental results show that the proposed method has strong anti-interference performance in the data set under complex scenes, which effectively reduces the false detection rate and improves the accuracy of smoke detection.