Intelligent automotive trade market forecasting and decision-making system: multi-source data fusion under meta-learning

With the continuous advancement of intelligent vehicle technology, the smart automotive trade market has become increasingly complex and dynamic.This study proposes an intelligent forecasting framework that integrates multi-source data through a meta-learning-based approach.Specifically, a meta-attention gated recurrent unit (MA-GRU) model is developed to enhance the accuracy and robustness of market predictions.The model first extracts key automotive performance indicators and market-related features using a GRU network, and then applies an attention mechanism to capture the most informative temporal dependencies.To address the challenge of data scarcity in the emerging smart vehicle market, meta-learning is introduced to improve the model's adaptability and generalisation across diverse datasets.Experimental evaluations demonstrate that the proposed MA-GRU framework achieves superior predictive performance even under limited data conditions, providing a solid technical foundation for market trend analysis and strategic decision-making in the intelligent automotive trade.