Moving obstacles,unlike stationary ones,cannot be located or avoided by traditional navigation technologies.To address this,a collision avoidance navigation path planning strategy for blind people based on deep learning is proposed.First,a speech recognition system is used to collect speech signal and sort out the speech feature parameters,which is then analyzed to obtain the speech sequence input thus recognize the destination.Second,an obstacle detection model is constructed to detect the edge features as well as moving directions and velocities of the obstacles on the path to destination.Then a convolutional neural network of deep learning is exploited to plan the optimal path with collision avoidance.Finally,experiments are conducted and the results show that the radial velocity of the moving obstacles detected by this model is consistent with actual conditions,specifically,when the actual speed is 33.6 cm／s,the detected speed error is in the range of 0.2-0.4 cm／s,and the accuracy of obstacle avoidance reaches 96.5％ when the test time lasts 50 min.It can be concluded that the proposed strategy can realize the optimal path planning and navigation with collision avoidance for the blind people.