<p>The impact of emergency events on the stock market cannot be underestimated, as their unpredictability poses significant challenges to investors’ stock operations. This calls for researchers and investors to seek more effective features and reasonable methods to mitigate risks. In the context of multi-feature prediction methods, analyzing the correlation between multi-dimensional features or data has always been a challenging issue. This paper proposes a stock market index prediction framework based on an encoder-decoder architecture (MF-EDNet). The framework leverages the dynamic correlation between stock data and futures data as prior knowledge, integrating features of both internal sequences (industry indices) and external sequences (futures data) to capture the impact of emergency events on the stock market. The newly proposed Multi-Dimensional Convolutional Attention Module (MCAM) further enhances the feature extraction and attention capabilities of the attention mechanism. Experiments on multiple industry indices in the Chinese stock market demonstrate that MF-EDNet can effectively extract important features from stock and futures data, exhibiting good predictive performance under emergency events. The proposed MF-EDNet model achieved improvements of 35.8% and 22.9% in the Matthews correlation coefficient (MCC), a 3.3% increase in accuracy (ACC) and a 7.86% enhancement in profit compared to previous state-of-the-art methods.</p>