Aiming at the problem that background of forest fires is complex

image fitting mode is subpar

which make feature description is inadequate

resulting in low recognition rate of convolutional neural network（ CNN） and random initialization of convolution kernel results in increasing iteration number. A three-channel fitting improved CNN forest fire identification algorithm is proposed. By adjusting the RGB three-channel ratio fitting sample images

the optimal combination of the contrast between the flame and the background is searched to solve the problem of recognition rate reducing. Principal component analysis（ PCA） algorithm is used to initialize the convolution kernel to improve the model for enhancement of the iteration rate. The experimental results show that the algorithm can improve the recognition rate and boost the iteration speed

the recognition rate reaches 98. 5 %.