Web-based optimization algorithm for web-based platform course design in teaching reform of university mathematics

In this paper, we first constructed a mathematics network course goal achievement index system with 5 primary indicators, 16 secondary indicators and 70 evaluation points to provide a scientific basis for course design.After that, based on the radial basis function (RBF) neural network structure, the fruit fly optimization algorithm (FOA) is introduced to dynamically optimize the parameters of the RBF model, and the dynamic FOA-optimized RBF neural network model is put forward to predict the degree of achievement of the course objectives.The results show that the model in this paper has good convergence and prediction accuracy, and its error on the four course math network goal attainment is only about 0.4%, with excellent model accuracy and simulation effect.Combined with the prediction results, considering the shortcomings of the current teaching, a blended teaching model based on mathematics majors is constructed, and the advantages of the teaching scheme in this paper are elaborated, which provides support for the teaching reform of mathematics courses.