The Detection of English Students’ Classroom Learning State in Higher Vocational Colleges Based on Improved SSD Algorithm

The current detection matrix of English students’ classroom learning status in higher vocational colleges is mostly a one-way processing form, and the detection range is small, resulting in an increase in the mean difference of unit detection. Therefore, this paper proposes a design and verification study on the detection method of English students’ classroom learning status in higher vocational colleges under the improved SSD algorithm. According to the actual detection requirements and the changes in standards, first extract the detection features of English learning status, expand the detection range by using a multi-objective approach, and design MTCNN multi-target detection matrix. Based on this, build a learning status detection model under the improved SSD algorithm, and use multi-level reduction correction to achieve status detection processing. The final test results indicate that the learning status of the selected 6 classes in the English classroom is detected, combined with an improved SSD algorithm. The final unit detection mean difference was well controlled below 1.5, and the detection accuracy for the five types of classroom behaviors remained above 90%, indicating that this learning state detection method has stronger pertinence and reliability, high detection efficiency, controllable errors, and practical application value.