Application of big data classification effects based on neural network in video English course and relevant optimization suggestions

Due to the improvement of Internet technology and information technology, more and more students hope to learn and consolidate knowledge through video in the classroom. Teachers are more accustomed to using video in the classroom to improve and improve their teaching quality. In the current English class, teachers and students are more accustomed to using video English for teaching. English teaching videos are informative, intuitive and efficient. Through video teaching, we can make the classroom atmosphere more interesting, thus simplifying complex problems. In this context, this paper analyzes how neural networks can improve the application effect of English video courses in the context of big data, optimizes the PDCNO algorithm by using the neural network principle, and then discusses the impact of the optimized PDCNO algorithm on classification and system performance. This improves the accuracy of English video, reduces the execution time of the algorithm and reduces the memory occupation. Compared with ordinary video, the training time required under the same training parameters is shorter, and the convergence speed of the model itself will be faster. From the students' attitude towards video teaching, we can see that students prefer video English teaching, which also reflects the effectiveness of neural network big data in English video teaching. This paper introduces the neural network and big data technology into the video English course to improve the teaching effectiveness.