Construction of Employment Prediction Model Based on Association Rules and Optimized RBF Neural Network

The continuous improvement of internet technology has promoted the application of big data prediction models in employment prediction, providing more diversified solutions for research and analysis in this field. This paper presents an employment prediction model that combines association rules with an optimized RBF neural network. This model is designed to predict and analyze academic performance and employment situations more efficiently and accurately. By comparing the performance of different models, it can be seen that the employment prediction model constructed in this article has smaller prediction errors; In addition, the analysis of the impact of different impact projects on the most employment rate and the correlation between each project also indicates that there are certain differences in the impact of different projects on the employment rate, and the degree of correlation between each impact project also varies to a certain extent. The model constructed in this article can achieve higher accuracy, accuracy, and sufficient reliability, providing new ideas and research methods for predicting and analyzing academic performance and employment rates in the field of education.