Objective. Developmental Dyslexia (DD) is a special learning disability (SLD), in which it is difficult for children to read, spell and write fluently, seriously affecting children’s related reading skills. Therefore, it is significant for early intervention to quickly and accurately detect children with DD in a no-feeling method. However, most existing detection methods include oral and written assessments, analysis based on brain image (e.g. MRI, fMRI, EEG, etc.), which either require the involvement of domain experts or have low accuracy and are not conducive to large-scale applications. Approach. In this study, inspired by computer vision, we present a novel convolutional-transformer network architecture based on Eye-tracking to improve the diagnosis accuracy of developmental dyslexia in children, which is evaluated on 187 subjects by tracking their eye movements while reading. Main results. To our knowledge, the proposed model achieved a state-of-the-art (SOTA) classification accuracy of 98.21% on the cross-subject dyslexia detection task for the public dataset SDUETDR. Significance. The proposed model can be used in schools as a diagnostic and screening tool for children with developmental dyslexia.