Radar emitter signal sorting （RESS） plays a critical role on modern electronic warfare. To address the limitations of traditional sorting methods， such as low efficiency and limited accuracy in complex electromagnetic environments， as well as radar-communication spectrum sharing （RCSS） brought about by the development of 5G communication technologies， this paper proposes an innovative solution based on multi-task learning and multi-domain feature fusion. First， the proposed method constructs a multi-task learning （MTL） framework to jointly preprocess noisy radar-communication mixed signals， including signal cleansing， denoising， and time-frequency feature enhancement. Second， a feature fusion network is designed to extract multi-scale features from both the time-frequency image domain and the time-delay-Doppler domain. An improved iterative attention mechanism is employed to achieve cross-domain feature fusion， resulting in a high-dimensional feature representation with a strong discriminative power. Finally， a clustering model based on DeepCluster is used to accurately sort radar emitter signals. Experiments demonstrates that under low signal-to-noise ratio （SNR） condition of -6 dB， the proposed method effectively suppresses communication signal interference and achieves a sorting accuracy of 93.8%. Compared with existing approaches， the proposed solution demonstrates significant advantages in enhancing the robustness of signal preprocessing and maintaining high sorting accuracy， underscoring its strong potential for practical engineering applications.