A flame image detection method is proposed based on convolutional neural network using serial feature fusion model with maximal relevance and minimal redundancy (MRMR) to address the issue that the flame recognition model based on shallow features is susceptible to environmental changes and has low robustness. First, to obtain more global features from the finite sample set training convolutional neural network, the pre-training method was used to extract the deep features from the flame image for serial fusion. Then, to solve the problem of high dimensions of fusion feature, large redundancy, and lack of dynamic features after fusion, the MRMR feature-selection algorithm was used to remove features with low relevance to the flame, obtain highly relevant serial features, and merge with dynamic features to obtain a superior subset of the reconstructed feature vector. Finally, the flame target was detected using the support vector machine classifier. Experimental results show that the proposed method has good generalization ability and flame detection capability.