In the context of neural network-based radar feature extraction and detection methods, single-feature detection approaches exhibit limited capability in distinguishing targets from background in complex environments such as sea clutter. To address this, a Multi-Feature Extraction Network and Graph Fusion Detection Network (MFEn-GFDn) method is proposed, leveraging feature complementarity and enhanced information utilization. MFEn extracts features from various time-frequency maps of radar signals to construct Multi-Feature Graph Data (MFG) for multi-feature graphical representation. Subsequently, GFDn performs fusion detection on MFG containing multi-feature information. By expanding the feature dimension, detection performance is further improved. Experimental results on dataset composed of real measured IPIX data demonstrate that MFEn-GFDn detection probability is approximately 8% higher than that of the Dual-Channel Convolutional Neural Network (DCCNN). Additionally, MFEn-GFDn enhances detection performance by expanding the feature dimension, particularly in environments lacking corresponding training samples.