Radar signal sorting is a critical step in electronic reconnaissance. To address the lack of correlation between temporal and spatial information in radar signal sorting, we propose the spatial and temporal hypergraph contrastive learning architecture (STH-CL). This architecture utilizes a Gaussian mixture model (GMM) to construct a spatial hypergraph based solely on pulse positions in feature space. It integrates the GMM prior into the attention mechanism of a lightweight Transformer to extract temporal features, thereby establishing a temporal hypergraph using K-Nearest Neighbors (KNN). The complementary contrastive learning improves hyperedge quality and enables accurate sorting. Experiments on simulated radar parameter data demonstrate that STH-CL achieves over 90% sorting accuracy.