While traditional visual SLAM has achieved amazing results, it is difficult to achieve the desired results in challenging environments.Deep learning has promoted the rapid development of computer vision. Combining deep learning with vision-based SLAM is a hot topic. Starting from the classical neural network of deep learning, this paper introduces the combination of deep learning and traditional vision-based SLAM algorithm.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 pointed out, which can help the autonomous mobile robot to be truly independent in the future, and the development of VSLAM in the future is prospected.