In this Letter, we propose an adaptive digital classifier for flow contrast enhancement in optical coherence tomography angiography (OCTA). To solve the depth dependence in the initial motion-based classification, a depth-adaptive motion threshold was determined by performing a histogram analysis of an en-face image at each depth and identifying the static and dynamic voxel populations through fitting. In the follow-up shape-based classification, to adapt to the deformed vessel shapes in OCTA, a modified vesselness function along with an anisotropic Gaussian probe kernel was defined, and then a three-dimensional (3D) Hessian analysis-based shape filtering was utilized for effectively removing the residual static voxels. The experimental outcomes validated that the proposed adaptive digital classifier enabled a superior flow contrast by combining both the motion and 3D shape information.