Aiming at the problems of incomplete foreground extraction, low accuracy, and high false detection rate of the existing video image flame detection algorithms, a video flame detection algorithm based on improved Gaussian mixture model (GMM) and multi-feature fusion was proposed. Firstly, for background modeling, an improved GMM method with adaptive Gaussian distribution number and learning rate was proposed to improve the foreground extraction effect and algorithm real-time performance. Then the flame color characteristics were used to filter out the suspected flame regions, and local binary pattern texture and edge similarity features were used for flame detection. Based on support vector machine, a flame fusion feature classifier was designed and compared. Experimental results on the public datasets show that the algorithm proposed in this paper effectively improved the background modeling effect. The flame detection accuracy reached 92.26%, and the false detection rate was as low as 2.43%.