Research on Predictive Analysis of Public Courses and Graduation Grades in Universities Based on GA-BP Neural Network

Learning public courses in universities has a significant influence on the learning of professional courses, average graduation credit scores, further education, and going abroad in the later stages of university. It is important to analyse the learning situation of students in public courses and predict their future learning based on these learning data. This article takes the performance data of 12 public courses of all undergraduate students from a university of science and technology from 2010 to 2015 as the research object. Through data processing and establishing a GA-BP neural network model, the average graduation credit score of students is predicted. The results indicate that the overall error between the predicted value and the true value is relatively small, and the prediction is more accurate, proving the importance of university public courses and the feasibility of the GA-BP neural network algorithm. Finally, the article compares this algorithm with multiple linear regression and a BP neural network using two evaluation indicators, RMSE and R Squared, and the results show that this algorithm has the highest accuracy.