Prediction of College Student Employment Characteristics Based on Deep Manifold Learning

Abstract

The employment of college students is of great significance to the development of the national economy. Due to the information gap between enterprisesand universities, the use of advanced artificial intelligence technology to analyzeand predict student information can provide reliable employment positioningreferences and effectively alleviate the current tight employment situation for college students. To improve the accuracy of predicting college students’ employment trends, a deep learning network based on the geometric structure of datamanifolds is proposed for forecasting employment characteristics. First, employment data from a university were collected and normalized, and covariance featurematrices were computed to
form a Lie group manifold. Second, by integrating differential geometry theory with deeplearning principles, a deep dataset recognition network based on Riemannian manifolds was developed. During model training, feature weight coefficients and learning rates were dynamically adjusted to obtain the optimal network model. Experimental results demonstrate that the deepmanifold learning algorithm achieves a 5.52%improvement in prediction accuracy compared to conventional convolutional neural network models, while also exhibiting higher efficiency and robustness. The predictive outcomes of this model can provide valuable insights for university employment departments, improving the foresight, targeting, and precision of
employment-related initiatives.

Keywords: college student employment; Lie group manifold; deep manifold learning;employment prediction

Cite As

W. Su, "Prediction of College Student Employment Characteristics Based on Deep Manifold Learning",
Engineering Intelligent Systems, vol. 34 no. 4, pp. 537-543, 2026.