A stereoscopic video generation method based on 3D convolution neural network is disclosed, which is able to convert existing 2D video sources into stereoscopic videos. The method includes preparing the training data, dividing the training video sources into left eye view sequences and right eye view sequences; and then processing the left eye image sequences through shot segmentation via fuzzy C-means clustering method, calculating a mean image of all left eye images, subtracting the mean image from the left eye images, taking the right eye images as a training target; training the obtained 3D convolution neural network through the training data; processing the 2D video sources which need to be converted into stereoscopic videos in the same way as training set, inputting to the trained 3D convolution neural network to obtain the right eye view image sequences of the 2D videos; and finally combining the two to be stereoscopic videos.