In order to solve the difficult problem of low target recognition rate caused by image blur in the laser rang-gated imaging process, we propose the keep-feature convolutional neural network (KFCNN) model for the target recognition of laser rang-gated images. Different from the convolutional neural network (CNN), the KFCNN model is used to improve the recognition rate of blurred targets and the robustness of target recognition with a new keep-feature layer. To achieve keep-feature in the KFCNN model, we optimize the keep-feature objective functions and the training by imposing keep-feature constraints and regularization. In addition, the feature maps of training samples are kept consistent before and after image blur when the value of the keep-feature objective function is reduced. The experimental results show that KFCNN improves the problem of recognition rate reduction caused by image blur and further improves the recognition rate of specified targets in laser rang-gated imaging.