Phylogenetic tree construction serves as a fundamental technique for elucidating evolutionary branching patterns and a core methodology for inferring species relationships. However, existing phylogenetic inference tools have inherent limitations: traditional alignment-based methods exhibit exponential growth in computational resource consumption as genomic data scales, while alignment-free approaches often rely on overly simplified assumptions, restricting their practical applicability. The trade-off between accuracy and efficiency remains a fundamental bottleneck in modern phylogenetic research. To address this challenge, we propose CS-Phylo, an innovative evolutionary distance estimation method that integrates closed-syncmer substrings with the MinHash algorithm, and leverages GPU computing to enhance computational efficiency. CS-Phylo employs a mathematically formalized anchoring mechanism to effectively select sequence features based on Closed-Syncmers, transforming long genomic sequences into compact CS-Sketch for efficient evolutionary distance matrix computation. Experimental results demonstrate that CS-Phylo exhibits significant advantages over existing tools in phylogenetic tree construction, achieving higher accuracy while maintaining computational efficiency. Additionally, by utilizing GPU acceleration, CS-Phylo effectively handles datasets of varying scales, making it a robust solution for large-scale phylogenetic analyses.