Stock price forecasting poses a formidable challenge in quantitative finance due to nonlinear market behaviors, high-dimensional feature spaces, and temporal volatility. Despite advancements in deep learning for financial time-series modeling, integrating temporal patterns, frequency-domain insights, and stock market interactions remains complex. We propose the Time-Frequency-Adaptive Correlation Network (TAC-Net), an innovative framework for A-share market forecasting, predicting t + 4 day stock returns through market-state perception, frequency-aware modeling, and inter-stock relationship mining. TAC-Net features market-aware temporal attention for joint modeling of time dependencies and market conditions, Fourier-enhanced modules for multi-scale periodic signal extraction, a stock relation module capturing market-driven dependencies, and an IC-weighted loss function optimizing predictive accuracy and ranking performance. Evaluated on daily CSI 300 and CSI 800 data (2010–2025), TAC-Net surpasses baselines like LSTM, Transformer, and MASTER across metrics including IC, ICIR, RIC, RICIR, AR, and IR, demonstrating its theoretical rigor and practical efficacy for quantitative investment and risk management.