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Anchoring is widely considered one of the most robust and consistently observed effects in experimental psychology. This study employs the highest and lowest indices of the Shanghai Stock Exchange (SSE) alongside the highest and lowest bullish sentiments over a 52-week period as anchors, in conjunction with Fibonacci retracement levels, to develop a dual market–sentiment anchoring multivariate feature matrix. Based on this feature matrix, we propose a forecasting model called Market Sentiment Dual Anchoring CNN2D-ABiLSTM (MSD-CNN2D-ABiLSTM). This model employs CNN2D to extract spatial features from market and sentiment data, utilizes BiLSTM networks to process and integrate temporal features, and incorporates an attention mechanism to emphasize essential spatial and temporal information. Experimental results indicate that this model achieves a prediction accuracy exceeding 90% and an R2 value greater than 95% for lags of 1–2 trading days, enabling precise forecasting of the SSE index. Additionally, the model demonstrates effective forecasting performance for up to 10 trading days ahead, significantly outperforming traditional baseline models. Furthermore, structural sensitivity tests reveal that the extraction of local spatial features by CNN2D provides a predictive advantage over the short-term temporal features captured by CNN1D in complex market structures.