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Aiming at the problem of irregularity of the pest area in the aerial images taken over forest area (discussed as forestry pest images in the following) and the poor generalization ability of traditional recognition method, a method for pest image segmentation based on full convolution network was proposed to realize automatic recognition of pest area. Firstly, the insect image of the forest area was needed to be obtained by using the eightrotor UAV aerial photograph technique over the pest forest area, and the pest area was marked with pixels for model training. Secondly, the full connection layer of the VGG16 model was replaced with the convolutional layer, and an endtoend study was used by implementing up sampling; and then the pretraining convolutional layer parameters were employed to improve the convergence speed of the model; finally, the skip layer was used to fuse a variety of feature information, which effectively improved the recognition accuracy, and five convolutional networks was constructed by this method. Experiment results showed that FCN-2s had the highest recognition accuracy among the five fullconvolution networks for forestry pest images. The pixel accuracy of the segmentation results was 97.86%, the mean crossover ratio was 79.49%, and the segmentation time for single image was 4.31s. Compared with Kmeans, pulse coupled neural network and composite gradient watershed algorithm, its pixel accuracy was higher by 44.93, 20.73 and 6.04 percentage points, respectively, the mean intersection over union towered above 50.19, 35.67 and 18.86 percentage points, and its segmentation time for single image was reduced by 47.54s, 19.70s and 11.39s, respectively. This method can realize the rapid and accurate recognition of pest area in aerial forest areas, which provide a basis for pest detection and prevention in forest areas.