ArticleScientific reports2026
LSTM-CNN hybrid model for E-commerce talent demand prediction and intelligent program optimization in vocational colleges under the double first-class initiative.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study develops an intelligent e-commerce talent demand prediction system built on a hybrid Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architecture, designed to support vocational college program optimization under China's Double First-Class Initiative. The hybrid model pairs LSTM networks for temporal sequence modeling with CNN components for spatial feature extraction, processing multi-dimensional data drawn from job platforms, industry reports, and employment statistics. Experimental validation confirms that the hybrid approach outperforms traditional methods, with a 32.4% reduction in MAE, a 28.7% improvement in RMSE, and an R
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