Single-layer diffractive neural networks often face classification accuracy bottlenecks due to limited wavefront modulation capabilities. Edge detection, as an optical image processing technique, extracts image contours and offers a promising way to simplify classification tasks. However, integrating edge detection and DNN-based classification on a single chip remains a challenge. Here, we propose an integrated nonlocal meta-platform that sequentially achieves all-optical edge detection and classification via spin-multiplexing. By exploiting the dispersion properties of the nonlocal Huygens’ metasurface, the co-polarized component in the output light performs momentum-space filtering for real-time edge detection. Subsequently, the cross-polarized component undergoes geometric phase modulation to execute image classification within the DNN. We couple quasi-bound states in the continuum and magnetic dipole resonances in crescent-shaped nanopillars, achieving a high polarization conversion efficiency of approximately 55%. This edge-enhanced DNN architecture significantly reduces data redundancy, elevating the classification accuracy of the single-layer network on the MNIST dataset from 64.2% to 80.7%. Our work provides a compact, high-efficiency solution for integrated all-optical machine vision and intelligent photonic computing.