Against the backdrop of electricity market liberalization and deregulation, accurate electricity price forecasting is critical for optimizing power dispatch and promoting the low-carbon transition of energy structures. However, electricity prices exhibit inherent complexities such as seasonality, high volatility, and non-stationarity, which undermine the efficacy of traditional forecasting methodologies. To address these challenges, this study proposes a four-stage Decomposition-Reconstruction-Optimization-Integration (DROI) framework, coupled with an econometric breakpoint test, to evaluate forecasting performance across distinct time segments of Spanish electricity price data. The framework employs CEEMDAN for signal decomposition, decomposing complex price sequences into intrinsic mode functions to retain essential features while mitigating noise, followed by frequency-based data reconstruction; integrates the Improved Sparrow Search Algorithm (ISSA) to optimize initial model parameters, minimizing errors induced by subjective factors; and leverages Convolutional Neural Networks (CNN) for frequency-domain feature extraction, enhanced by an attention mechanism to weight channels and prioritize critical attributes, paired with Long Short-Term Memory (LSTMs) for temporal sequence forecasting. Experimental results validate the method’s robustness in both interval forecasting (IPCP = 100% and IPNAW is the smallest, Experiment 1.3) and point forecasting tasks (MAPE = 1.3758%, Experiment 1.1), outperforming naive approaches in processing stationary sequence clusters and demonstrating substantial economic utility to inform sustainable power system management.