Electromagnetic （EM） battlefield has become increasingly complex due to the proliferation of heterogeneous communication systems， diverse radar waveforms， and a wide array of data link protocols. Accurate and real-time spectrum situation awareness critically depends on the effective extraction of discriminative features from multi-source， multi-modal EM signals and their fusion into consistent， high-level representations—enabling robust classification and radiation source identification. To address these challenges， this paper proposes a comprehensive recognition framework integrating spectral feature parameter extraction， modulation recognition， protocol identification， and multi-source heterogeneous data fusion. The framework achieves high-fidelity signal characterization under low signal-to-noise ratio （SNR） conditions. First， a hierarchical modulation recognition method is developed based on envelope characteristics， spectral symmetry， and spectral peak count， enabling reliable discrimination among five representative signal types—SSB， FM， FSK， MSK， and AM—as well as TACAN signals. Second， domain-specific communication system features are extracted to construct a data link recognition model with enhanced interpretability and generalization. Third， to handle multidimensional spectral feature fusion， a signal preprocessing pipeline and a dimensionality-reduction fusion model are designed to preserve salient information while reducing redundancy. Furthermore， transfer learning and few-shot learning strategies are integrated to mitigate performance degradation under limited and imbalanced training samples for novel radiation sources. Extensive simulations demonstrate that the proposed framework maintains high recognition accuracy across diverse SNR levels and exhibits strong robustness and generalization capability， effectively overcoming the challenges of low-data regimes and class imbalance.