Research on the improvement effect of machine learning and neural network algorithms on the prediction of learning achievement

In order to improve the effect of college student performance prediction, based on machine learning and neural network algorithms, this paper improves the traditional data processing algorithms and proposes a similarity calculation method for courses. Moreover, this paper uses cosine similarity to calculate the similarity of courses. Simultaneously, this paper proposes an improved hybrid multi-weight improvement algorithm to improve the cold start problem that cannot be solved by traditional algorithms. In addition, this paper combines the neural network structure to construct a model framework structure, sets the functional modules according to actual needs, and analyzes and predicts students' personal performance through student portraits. Finally, this paper designs experiments to analyze the effectiveness of the model proposed in this paper. From the experimental data, it can be seen that the model proposed in this paper basically meets the expected requirements.