Prediction method for foreign language teaching achievement utilizing an enhanced BP neural network

With the rapid development of digital technology, online learning has become an important way for students, especially those majoring in foreign languages, to learn. To improve the accuracy of predicting student grades in foreign language teaching, a backpropagation neural network optimization model based on particle swarm optimization algorithm is proposed. Starting from the analysis of students' online learning behavior sequences, this study extracts the characteristics of online learning behavior sequences, optimizes the weights and biases of backpropagation neural networks through particle swarm optimization algorithm, and constructs a student performance prediction model. The experimental results showed that the mean square error of the proposed model in the third round of training was 0.1834, significantly lower than that of the comparison model. In practical applications, the model shows good performance in predicting the grades of three classes, with an average prediction accuracy of over 80% for Class one, Class two, and Class three, which are 81.25%, 82.03%, and 86.07%, respectively. The experiment shows that the proposed model has a high prediction accuracy. The study provides an effective method for predicting grades for foreign language educators, which helps teachers adjust teaching strategies in a timely manner based on students' learning situations.