Construction of Youth Night School Education Model in Higher Vocational Colleges Based on Multi-Layer Perceptron Algorithm and Its Empowerment Path for Local Economy
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Abstract
Higher vocational colleges possess resource endowments for skill-oriented community education. Taking such colleges as the primary organizers of Youth Night Schools helps expand the service boundary of vocational education and strengthen the supply capacity of skill training for urban young people. To tackle two prominent obstacles—the lack of quantitative evaluation tools for educational models and ambiguous mechanisms of local economic empowerment—this study proposes an evaluation framework centered on the Multi-Layer Perceptron (MLP) algorithm. An indicator system is established covering curriculum design, faculty allocation, practical training facilities and other dimensions. A neural network architecture is constructed, where hidden-layer feature extraction enables nonlinear fitting of operational performance. Combined with an analysis of industrial talent demand, the correlation between evaluation outputs and regional employment structure is further explored. The proposed evaluation model delivers favorable fitting accuracy and generalization capability, which can provide decision-making support for higher vocational colleges to optimize curriculum provision and deepen college-local coordination.
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