Research on Automatic Coloring Technology of 2D Animation Based on Deep Learning as a Teaching Tool

With the growth of digital content, the demand for 2D animation is also increasing, and the traditional manual coloring can no longer meet the efficiency requirements. An automatic coloring method based on deep learning suitable for teaching is proposed. It is based on convolutional neural network, including residual enhancement and style embedding module to improve feature extraction and realize multi-style adaptation, and help students understand style transfer and semantic control. By combining semantic segmentation for edge detection, this method improves the accuracy of image structure and supports end-to-end learning. Experiments show that this method is superior to the existing technology in preserving character features, restoring details and tone consistency, and provides practical tools and real cases for students majoring in animation and computer. This study not only expands the application of deep learning in digital art creation but also provides practical experience for cultivating innovative talents in colleges and universities.