In this paper, we use Bayesian model to calculate color probability, train Bayesian classifier using a skin color training set of different gestures, and realize full skin color foreground object segmentation. The rendering of depth texture is established throughout to realize the unified scene rendering of art sketching teaching space and combined with a deep convolutional neural network to realize the interactive operation of virtual reality sketching teaching. Sampled students of T1 and T2 groups of art majors in Q College were analyzed for the acceptance of the virtual teaching scene of art sketching and explored the usability of virtual teaching of art sketching. Analyze and test the application effect of virtual teaching of art sketching from the two aspects of student’s learning motivation and the results of sketching work innovation assessment. The virtual simulation course of art sketching is designed to meet demand innovation by combining the total score of SUS and the application results. Based on the total SUS score of 62.325 for virtual teaching of art sketching, we added expert scoring data in the course design and obtained the difference between student scoring and expert scoring in terms of ease of operation, R42, and smoothness of operation and interaction, R23, which are only 0.01 and 0.06 respectively, which indicates that the virtual simulation course of art sketching should pay more attention to ease of operation and smoothness of operation. It shows that the virtual simulation course for art sketching should pay more attention to the ease of teaching and smoothness of operation and interaction.