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Due to fire detection is relatively low in the case of complex indoor environment,the proposed support vector machine (SVM) is applied to fire detection in the paper,among which an improved particle swarm optimization (PSO) is used to determine optimal parameters of support vector machine. Firstly,the obtained flame image will be processed ahead of time and extracted related feature quantity after flame image segmentation in YCrCb color space. Secondly,the optimal kernel parameter and penalty factor for support vector machine will be found by PSO algorithms,meanwhile,the ability of searching accuracy and speed of the optimal parameters of SVM are raised by adding mutation and nonlinear dynamic adjustment inertia weight in PSO algorithm;Then,each extracted flame characteristic parameters is reserved as training samples to train the SVM model,meanwhile,the SVM classifier model is established after the optimization of the parameters. Finally,the test samples will be input the SVM model to classification and recognition. The accuracy rate of algorithm is 94.09%,and the classification effect is better than other algorithms. Simulation results show that the improved SVM algorithm optimized by PSO can enhance the accuracy and real-time performance of flame recognition,as the same time,the algorithm has better adaptability and lower false positive rate.