Task degree has become one of the important indicators to measure students’ English learning intensity and learning quality, and the difference in task degree has different effects on students’ English learning. In order to realize the task recognition of English classroom teaching, combined with the characteristics of deep learning, this study combines the actual situation of English classroom teaching to analyze, and distinguishes characters through student positioning and feature recognition. Moreover, this paper combines the characteristics of English learning scoring to judge students’ learning situation, and designs a shallow convolutional neural network based on TensorFlow architecture for identifying images and uses GPU training acceleration to solve the problem of training time-consuming in the face of large data volume. In addition, the task results feedback is evaluated by scoring method, and the performance of the algorithm is analyzed by experiments. By setting the category of sensitive targets, this paper can perceive the results according to the target location and mark the sensitive targets in the input scene image. The research results show that the method proposed in this paper has certain effects.

中文翻译：

---

基于深度学习框架和图像目标识别的英语学习任务程度分析

任务程度已成为衡量学生英语学习强度和学习质量的重要指标之一，任务程度的不同对学生英语学习的影响也不同。为了实现英语课堂教学的任务识别，结合深度学习的特点，本研究结合英语课堂教学的实际情况进行分析，并通过学生定位和特征识别来区分字符。此外，本文结合英语学习评分的特点来判断学生的学习状况，设计了基于TensorFlow架构的浅层卷积神经网络来识别图像，并利用GPU训练加速来解决面对训练时费时的问题。大数据量。此外，通过评分方法对任务结果反馈进行评估，并通过实验分析算法的性能。通过设置敏感目标的类别，本文可以根据目标位置感知结果，并在输入场景图像中标记敏感目标。研究结果表明，本文提出的方法具有一定的效果。