The layer poultry farming industry urgently requires an early-stage, non-invasive and high-precision technique for sex determination of hatching eggs. Developing an innovative strategy for acquiring sex-related information based on volatile organic compounds (VOCs) detection represents a key breakthrough in this field. This study introduces a novel approach for early non-destructive sex determination through deep learning-assisted olfactory visualization technology. By simulating biological olfactory mechanisms, we developed a cross-sensitive colorimetric sensor array chip employing gender-specific VOCs-responsive dyes as sensing elements. The methodology involves extracting RGB signal differentials from individual sensing units before and after reactions to establish a multidimensional "gas fingerprint". Subsequently, a predictive model for sex determination was constructed by integrating convolutional neural networks (CNNs), achieving non-destructive gender determination with more than 90% accuracy on the 7th day of egg incubation. This method provides a novel tool for intelligent incubation systems and significantly enhancing the automation and intelligence levels of layer poultry farming.