The Construction of Blended Teaching Model of College English in Applied Colleges and Universities Based on Neural Networks

This paper provides strong support for the construction of online and offline blended English teaching modes. Firstly, it applies the parallel K-means clustering algorithm of the adaptive cuckoo search to cluster and analyze the data related to blended English teaching. This process results in a series of processed teaching data, which is then used to design a blended English teaching evaluation index system for college students. The teaching data obtained is used to design the system. Aiming at the shortcoming that there is no theoretical basis for the selection of many parameters in the training process of the BP neural network, a teaching quality evaluation model based on the GA-BP neural network is constructed. Then, the blended teaching model is constructed based on this evaluation model. Through empirical experiments, it was found that the overall mean English achievement of the experimental group increased by 19.2% after the implementation of the hybrid teaching model, which was 6.7% higher than that of the benchmark group. The results demonstrate that the blended teaching model, which is based on the GA-BP blended teaching quality evaluation model, enhances college students’ English performance and is suitable for widespread application in college English teaching.