This paper uses a convolutional neural network to automatically classify microscopic images of hair evidence with the aim of enhancing the automation of microscopic technology and providing technical reference for test efficiency. Six kinds of microscopic hair images are collected via Leica DVM6 microscope and are magnified 1400 times to form the sample image dataset which contains 60000 images. The network model Hair-Net based on the convolutional neural network is used to conduct sample training and testing using different parameters. Experimental results show that the classification accuracy of improved Hair-Net can reach 97.82% after parameter testing and optimization, demonstrating that this method can realize automatic classification of microscopic hair images and enhance the robustness.