To solving the problem that the image segmentation algorithm based on active contour cannot effectively segment the images of Ochotona curzoniae with small target, complex background and insignificant features，the SegNet semantic model based on convolution neural network was used to segment the images of Ochotona curzoniae. Firstly, the images of Ochotona curzoniae were preprocessed to make data set consistent with Pascal VOC data set format after scale normalization. Then, the data set was divided into training set and testing set. The training set was used to train the SegNet model, and the testing set was used to estimate the performance of SegNet model. The experimental results of images segmentation for Ochotona curzoniae show that compared with the CV model based on active contour, the intersection over union, mean average precision, similarity index, and jaccard index of the SegNet semantic model based on convolution neural network improved 68.33%, 9.35%, 30.61% and 47.98%, respectively. The false positive volume function and false negative volume function of the SegNet semantic model based on convolution neural network decreased 87.20% and 16.52%, respectively.