To address the challenges of causal discovery in high-dimensional data, this paper proposes a significance-weighted divide-and-conquer causal discovery approach (SWCD). Traditional causal discovery methods suffer from high computational complexity, ambiguous differentiation of Markov equivalence classes, and crude conflict resolution mechanisms in high-dimensional scenarios. To overcome these limitations, this work integrates divide-and-conquer strategies with significance weighting to refine the causal discovery process. Specifically, the method achieves a synergistic optimization of efficiency and accuracy through a three-tiered design. In the partitioning phase, path significance values (PSV) and path importance scores(PIS) are defined to dynamically quantify the statistical reliability of causal paths. By combining topological features and adaptive partitioning strategies, the framework prioritizes retaining high-confidence causal chains to protect critical structures while dynamically optimizing decomposition paths. In the solving phase, the PC algorithm is enhanced using residual-based conditional independence testing (ReCIT), which distinguishes Markov equivalence classes by analyzing the independence of regression residuals. In the merging phase, a confidence score-driven conflict resolution mechanism is designed to resolve edge conflicts during subgraph merging, where edge reliability is quantified through confidence scores. Experimental results demonstrate that the proposed method significantly outperforms existing baseline approaches such as CPBG on high-dimensional datasets, achieving superior performance in efficiency, robustness, and interpretability. Future research can focus on optimizing significance quantification metrics, refining dynamic partitioning strategies, and exploring adaptability to nonlinear causal relationships.