Abstract: Spermatogenesis is an important bioprocess in the lifetime of male mammalians, which has deep effect on mammals reproduction. Abnormal spermatogenesis is a major cause of male infertility, however treatments for this are limited. Characterizing the genes/proteins involved in spermatogenesis is fundamental to understand the mechanisms underlying this biological process and to develop treatments for the problems in spermatogenesis. However, most crucial information of spermatogenesis-related genes/proteins scatters in vast amount of research articles, so manually curation of these genes/proteins could be a time-consuming task. In this paper, a novel strategy is proposed to automatically extract the names of spermatogenesis-related genes/proteins, which function in different stages of spermatogenesis based on literature mining. Firstly, it compares three different algorithms performance on different terms and applys an SVM classifier trained with a manually prepared dataset to classify spermatogenesis-related texts into three classes in accordance with the three stages of spermatogenesis. Then, integrating expert knowledge and grammar rules, it recongnizes and extracts the gene/protein names of each spermatogenesis stage with high confidence. Finally, a manually curation test dataset is used to test the performance of this strategy, and the strategy gets an accuracy of 71.9%, which verifys the reliability of proposed method and proves the value of application.