In order to improve the accuracy of segmentation and counting of insect images, an insect image segmentation and counting method based on convolutional neural network is proposed. Based on the U-Net model, this method constructs an insect image segmentation model named Insect-Net. After inputting the complete insect image and the split insect image into the model, the features of the two images are extracted and merged. The merged features are inputted into a 1×1 convolutional layer to get the final segmentation results. After the obtained results are binarized, the contour detection algorithm is used to extract the contours of the insects and count them. The experimental results show that the method has higher segmentation accuracy and counting accuracy in the detection of insects, which are 89.2% and 94.4% respectively. The idea of deep learning and convolutional neural network effectively improves the counting accuracy of insect images, and provides a large number of non-background datasets for insect identification classification.