Flame equivalence ratio measurement using flame color modeling method， is an emerging research direction in the combustion diagnosis technology. At present， the modeling methods mainly use the blue/green color features （B/G） in the RGB（Red-green-blue）model as the modeling input， however， the color equivalence ratio modeling by single color ratio fitting has large uncertainty and measurement errors. Therefore， this paper proposes to use the multi-color feature parameters under different-color models as the modeling inputs. Firstly， the digital flame color distribution （DFCD） technology is used to process the methane premixed flame image and obtain the region of interest （RoI） of flame images. Secondly， the flame color feature variables are comprehensively analyzed， and the multi-color features under different color models are designed and extracted， which are 36 color features. Then， the Spearman rank correlation analysis and random forest （RF） algorithm are used to screen out the deeper color features， and 16 dimensional high-quality features are selected. At last， the optimal support vector machine （SVM） parameters are selected using the grid search method （GSM）. Furthermore， the equivalence ratio measurement model of premixed methane flame is trained by SVM using the feature subset constructed. The algorithm is compared with the traditional BP neural network and the extreme learning machine （ELM） algorithm. Experimental results show that the algorithm has better regression prediction effect， and the mean square error （MSE） decreases to 0.023.