Although traditional vision-based SLAM (VSLAM) technologies have achieved impressive results,they are less satisfactory in challenging environments.Deep learning promotes the rapid development of computer vision and shows prominent advantages in image processing.It's a hot spot to combine deep learning with VSLAM,which is promising through the efforts of many researchers.Here,we introduce the combination of deep learning and traditional VSLAM algorithm,starting from the classical neural networks of deep learning.The achievements of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in depth estimation,pose estimation and closed-loop detection are summarized.The advantages of neural network in semantic information extraction are elaborated,and the future development of VSLAM is also prospected.