Application Analysis of Virtual Simulation Network Model Based on 3D-CNN in Animation Teaching

With the continuous progress of educational technology, the application of animation in teaching has gradually become an effective means to improve learning experience and effectiveness. This study focuses on the application of 3D-CNN virtual simulation network model in animation teaching, aiming to deeply analyze the impact of this model on the learning process and its potential advantages in improving learning effectiveness. 3D-CNN stands for Three-Dimensional Convolutional Neural Network. Unlike traditional 2D-CNN, 3D-CNN is specifically designed for processing 3D data, such as video and volume data. This type of neural network is very useful in processing temporal and spatial information, and is therefore widely used in fields such as video analysis and image processing. By reviewing the history and development of animation in the field of teaching, and based on previous research, a 3D-CNN virtual simulation network model is proposed, and its basic principles and applications in virtual simulation are analyzed. Through experimental analysis of teaching effectiveness, quantitative and qualitative indicators for evaluating academic performance were revealed, revealing the specific impact of the model on learning outcomes. Compared with previous research, we can find that the 3D-CNN virtual simulation network model has achieved good practical application effects in animation teaching. Based on the survey results on student participation and interest, the potential mechanisms of virtual simulation in improving student interest and active participation were explored. This study provides a reference for promoting the integration of animation teaching and 3D-CNN virtual simulation network models in the field of education.