The Resnet (Residual Neural Network) algorithm, proposed in 2015, has rapidly demonstrated strong application potential in multiple fields such as image recognition and natural language processing due to its unique advantages in solving gradient vanishing and model degradation problems in deep learning. In the field of education, these features of Resnet have been cleverly applied to various aspects such as student learning behavior analysis, knowledge graph construction, and personalized recommendation systems. By constructing deep learning models, Resnet can process complex educational data, extract hidden patterns and patterns, and provide data support for educational decision-making. For example, it can help teachers understand the learning process of students, predict their learning outcomes, and optimize teaching strategies. MATLAB simulation shows that the model based on ResNet algorithm has an effect on college English classroom teaching under certain evaluation criteria The evaluation accuracy and the authenticity of classroom teaching effect evaluation are better than traditional evaluation methods.