Abstract: Aiming at  the problem of low accuracy of traditional support vector machines (SVM), we proposed an LDBO-SVM model. Firstly, 
in order to solve the problem of uneven distribution of the initial solution of the original dung beetle optimization algorithm, the Logistic chaotic map was introduced into the algorithm to construct the LDBO algorithm. Secondly, the LDBO algorithm was used to optimize the internal penalty factor and kernel parameters of the traditional support vector machine, and the LDBO-SVM model was constructed. Finally, in order to verify the performance of LDBO-SVM model, LDBO-SVM model was compared with the 
improved SVM by using five other population intelligent optimization algorithms. The experimental results show that the accuracy of LDBO-SVM model reaches 94.53%, and  can accurately predict student achievement, providing assistance for  teachers to improve their teaching plans.

Key words: machine learning, support vector machine, dung beetle optimization algorithm, parameter optimization