The inequality between men and women has almost emerged with the emergence of human history, but gender stereotypes are displayed and promoted through media, and have a profound impact on the psychological cognition and behavior of the general public. The stereotype of women categorizes them as belonging to the humanities rather than the technical field, which leads to a dual impact of material and psychological factors on the innovative behavior of female employees. By analyzing text data on platforms such as the internet, news, and social media, implicit gender roles and stereotypes can be discovered. This article designs an analysis method based on deep learning (DL) technology to explore the impact of gender stereotypes on women in the technical field. Firstly, collect text data related to technical fields and gender stereotypes, process them, and then use DL technology to extract features related to gender stereotypes from the preprocessed data. Train classifiers or generative models using extracted features to identify and analyze implicit gender stereotypes in text. The experimental results indicate that using the method proposed in this article can provide a more comprehensive understanding of the formation and impact of gender stereotypes in the field of technology.