Tables are widespread in real-world scenarios. In particular, hierarchical tables encode much richer structural semantics; however, their complex layouts pose severe challenges to Large Language Models (LLMs). The core difficulty lies in precisely aligning complex topological structures with multi-level semantic dependencies, where alignment failures often lead to cross-level aggregation errors and hallucinations. To address this challenge, we propose STAR-Table, a framework that synergizes visual perception with symbolic reasoning. Unlike traditional text-only approaches, STAR-Table leverages OCR models to extract pixel-level spatial coordinates, thereby establishing explicit Spatial-Textual AnchoRs. These anchored spatial priors are formulated as explicit structural hints to guide LLMs in reconstructing the hierarchical topology of tables. Based on the reconstructed topology, complex visual layouts are decomposed into one or more distinct sub-tables, each of which is further mapped into a deterministic flattened database. Finally, we design an adaptive QA module that fuses Vision-Language Model (VLM) semantic summaries with multi-step SQL execution to support robust table-based reasoning. Experimental results across multiple benchmarks demonstrate that STAR-Table achieves superior performance, significantly outperforming specialized fine-tuned models and advanced in-context learning paradigms.