Traditional compliance checks rely on manual interpretation of regulatory clauses and encoding them into static rules, which face problems such as rule conflicts and delayed updates. This study constructs a domain ontology model to perform structured abstraction of data compliance specifications. By defining the “data subject-processing behavior-compliance goal” triple, regulatory clauses such as GDPR and Personal Information Protection Law are deconstructed into machine-readable semantic networks. Using knowledge engineering results as input, a hybrid reasoning engine based on the improved RETE algorithm is designed. The compliance inspection task is modeled as a directed acyclic graph (DAG), and inefficient paths are pruned through memorized search to reduce inspection delays in complex scenarios. An incremental checking mechanism is further introduced, and the mapping relationship between changed data characteristics and rule influence domains is used to achieve differentiated updates of local rules and avoid performance losses caused by re-analysis of full rules. The maximum rule matching delay of this method is only 119.4 ms, the false alarm rate is reduced, the rule matching accuracy is improved, and the missed alarm rate is between 1.9% and 6.7%, providing more reliable technical support for data compliance management.