In this paper we present a novel auto-probabilistic mining method designed to enhance the training process of Siamese neural networks. The proposed method demonstrates significant efficiency, particularly in scenarios characterized by limited data and computational resource. The study incorporates the application of the newly developed mining method in conjunction with a previously introduced auto-clustering technique. Comparative experiments are conducted both with and without the integration of the proposed method, highlighting its impact on overall performance. Additionally, the research introduces a novel metric loss to further refine the training process taking into account the drawbacks of existing loss functions applying for metric learning. During the experimentation phase, validation is conducted on the printed Hangul character PHD08 database and Omniglot. This validation provides valuable insights into the performance and effectiveness of the proposed methods.