An essential difference between traditional pattern recognition and biomimetic pattern recognition ( BPR) is reviewed. Different from the idea of “matter classification” of traditional pattern recognition, BPR considers the problem of pattern recognition as the“cognition” of every type of sample, uses the principle of“homology continui⁃ty” as a priori knowledge, and performs class recognition by a union of geometrical cover sets in high⁃dimensional space and feature space, thus overcoming the shortcomings of traditional pattern recognition. The effectiveness of BPR has gradually drawn extensive attention from scholars. In this study, research on BPR and its applications are summarized. The research method includes the topological analysis of the distribution of sample points, covering al⁃gorithm research, and a sample’ s attribute in the overlapping space. Applications of BPR involve object recogni⁃tion, biometric identification, text recognition, NIR spectroscopy qualitative analysis, and so on. Results show that BPR is an innovative and effective means of pattern recognition. Finally, important development directions of BPR are reported, such as manifold analytical methods of sample distribution in the same class, topological theory, and algorithm research in a high⁃dimensional space.%回顾了仿生模式识别与传统模式识别的本质区别，与传统模式识别“分类划分”思想不同，仿生模式识别把模式识别问题看成是各类样本的“认识”，并将“同源连续性”规律作为先验知识，用高维空间几何形体覆盖方法实现对同类事物的学习，因此克服了传统模式识别的缺点。其有效性逐渐受到学者的广泛关注。分析总结了目前已有的仿生模式识别方法的研究和应用，方法研究包括样本点分布的拓扑分析、覆盖算法和重叠空间中样本的归属；应用研究方面包括目标识别、生物特征识别、文本识别、近红外光谱定性分析等。分析表明仿生模式识别是创新、有效的模式识别方法。最后指出同类样本点分布流形的分析方法和高维空间拓扑理论与算法研究等是仿生模式识别未来重要的发展方向。
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