The identification of latent DNA binding domains presents both significant scientific value and analytical complexity, given the extensive diversity within biological compound datasets. To address this challenge, our research introduces weighted deep forest (WeighDF), a novel computational framework integrating hybrid feature representation with adaptive multi-granularity scanning analysis. This approach dynamically weights features across scanning windows using learnable attenuation coefficients, which amplifies key sequence patterns and suppresses background noise. For comprehensive prediction of diverse DNA binding patterns, we further develop decision learning predictive algorithm for binding sites (DecLPABS), an ensemble architecture combining WeighDF’s adaptive scanning with meta-learner integration strategies. This dual-phase system demonstrates superior versatility in handling both categorical classification and continuous regression problems. Empirical validation across heterogeneous datasets reveals DecLPABS’s enhanced predictive capability, achieving 0.8979 accuracy through optimized feature-space partitioning.