Innovative Optimization of College English Education Based on Neural Network Algorithm

The innovation and optimization of college English teaching based on a simulated neural network algorithm are studied in detail. A neural network algorithm for optimizing English teaching is constructed. Based on cognitive process simulation, the characteristics of current English teaching are applied to English teaching, and the influence of error on sentence analysis is eliminated. In the process, the latest network algorithm is used to calculate the detailed data of college English education. The convergence rate of the neural network algorithm is tested and analyzed, and the basic algorithm is identified in the changing trend of local problem test data. Experiments show that the data obtained based on the simulated neural network algorithm are more efficient. Traditional prediction models or machine learning methods include linear regression, decision trees, random forests, support vector machines (SVM), naive Bayesian classifiers, etc. These models have been used in the past for prediction or classification tasks in the field of education. However, the prediction model based on the simulated neural network algorithm proposed in this paper shows higher performance in accuracy, stability, and reliability, and significantly reduces interlayer errors.