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As satellite and communication technology advances, terrestrial-satellite communications and broadcasting (TSCB) provide uninterrupted services, meeting the demand for seamless communication and broadcasting interconnection. The evolving TSCB technology faces challenges in handling dynamic time-frequency features of wireless signals. Stable satellite-ground interaction is crucial, as co-channel interference can disrupt communication, causing instability. To address this, the TSCB system needs an effective mechanism to eliminate signal interference. Current methods often overlook complex domain features, resulting in suboptimal outcomes. Leveraging deep learning’s computational power, we introduce WSIE-Net, an encoder-decoder model for TSCB signal interference elimination. The model learns an effective separation matrix for robust separation amidst wireless signal interference, comprehensively capturing orthogonal features. We analyze time-frequency diagrams, bit error rates, and other parameters. Performance assessment involves similarity coefficients and Kullback-Leibler Divergence, comparing the proposed algorithm with common blind separation methods. Results indicate significant progress in signal interference elimination for TSCB.