Traditional navigation methods can only detect static obstacles on the path, but can not detect moving obstacles. Therefore, a collision avoidance path navigation method for blind people based on deep learning is proposed. The speech signal is collected, and the speech feature parameters are obtained by using the speech recognition model. According to the speech feature parameters, the content of the speech sequence input by the blind person is recognized, and the destination of the blind person is determined. The obstacle detection model is constructed to detect the shape features, moving direction and speed of the obstacles on the blind's location and their destination path, and calculate the distance between the initial position and the arrival position. The convolution neural network in deep learning is used to plan the optimal collision avoidance path and realize the blind collision avoidance path navigation. The experimental results show that the radial velocity of the obstacles detected by this method is almost the same on the x-axis and y-axis, and the moving direction and velocity of the obstacles can be tracked and monitored in real time. The speed obtained by this method is basically consistent with the actual speed of the obstacle, and the error is between 0.2 and 0.4cm/s. when the test time is 50min, the obstacle avoidance accuracy reaches 96.5%, which can achieve the optimal collision avoidance path planning and navigation.