Abstract Our Portable Adaptive Optics (PAO) system designed for high-contrast imaging of exoplanets with current 2-4 meter class telescopes achieves a correction speed of nearly 1000Hz, utilizing a Shark-Hartmann Wavefront Sensor (S-H WFS) in a $9\times9$ sub-aperture configuration. As we look towards adapting the PAO system for larger telescopes, an increase in the number of sub-apertures in the WFS and enhanced precision in wavefront detection are imperative. Originally programmed in LabVIEW, our initial PAO software is based on a traditional centroid calculation module for nighttime wavefront sensing and lacks adaptive processing of background noise. To address these limitations and to boost the PAO system’s performance and accuracy in wavefront detection, we propose a compressive neural network(Th-Net) combined with a specialized hybrid parallel programming approach for wavefront detection. Our experimental results indicate that this hybrid parallel technique and Th-Net significantly enhance the PAO system’s operational speed and wavefront detection precision under uneven background noise. This work paves the way that a duplicable and low-cost PAO system can be used for direct imaging of exoplanet with large telescopes.