Robotic grasping perception is a fundamental prerequisite for autonomous manipulation and embodied intelligence. The technical paradigm is undergoing a profound shift from analytical methods based on explicit geometric modeling to intelligent perception frameworks driven by data-driven learning and enhanced semantic reasoning. Research on robotic grasping perception was systematically reviewed along the lines of paradigm evolution. The evolutionary process was described through three progressive stages: analytical geometry-driven methods, visual data-driven methods, and semantic understanding and reasoning enhancement. Representative algorithms and key technical pathways for each stage were examined and analyzed. Through a comparative analysis of input modalities, data requirements, generalization ability, and task adaptability across different paradigms, the advantages and limitations of various methods in unstructured environments were summarized. Furthermore, the evolution of grasping datasets from planar benchmarks to large-scale comprehensive data was systematically traced, and the quantitative evaluation system composed of task reliability and proposal accuracy was analyzed. Prevailing challenges—including sim-to-real transfer, inference efficiency, cross-modal information fusion, and the extension to complex tasks—are identified. Future development trends that integrate embodied foundation models with dexterous manipulation are discussed to provide references for building general-purpose robotic grasping systems with high generalization performance and robust task comprehension.