Video analysis of intelligent teaching based on machine learning and virtual reality technology

In this paper, we study and analyse the teaching video of oil painting art through machine learning combined with virtual reality computing. Since the current oil painting, image acquisition method cannot meet the user's demand for multi-dimensional image description, and at the same time the retrieval method is too simple to perform high flexibility retrieval, we try to adopt the deep learning-based object extraction fusion method. Also, objects with poor performance quality are not suitable for further interception and cutting. We first pre-process the images in the image library to filter out the relatively high-quality images and then filter out the objects whose saliency and clarity do not reach the queue value by judging the saliency and clarity values of the images. Next, a series of aesthetic criteria, such as visual balance, visual triangulation, and centrosymmetric diagonal composition criteria, used to further filter the objects with relatively poor quality and low ratings. Then, we expand the areas with high saliency, match the contours of the segmented image elements with the contours of the user-drawn image to return an optimal matching value, and finally improve the quality and naturalness of the image by learning the deeper features of the image based on the style migration. The experimental framework based on TensorFlow is a new application of deep learning in the field of image synthesis, which has a very good improvement in the implementation efficiency compared with the traditional method. Using virtual reality technology to carry out teaching practice and analyse the effect of teaching practice, students can immerse themselves in art appreciation teaching activities, accept multiculturalism, learn through experience, and improve aesthetic quality.