To address the limitations of the Chameleon algorithm in terms of parameter sensitivity, noise robustness, and computational efficiency, this study proposed a statistical-MST integrated hierarchical clustering algorithm(SHCA) based on the minimum spanning tree and statistical features. The minimum spanning tree was used to construct a sparse graph, eliminating manual parameter intervention, and the global optimality of the minimum spanning tree was used to avoid false cross cluster connections. The dynamic statistical merging strategy was designed to filter the noise combined with the local distance threshold, and the sub clusters were merged iteratively through the inter cluster connectivity test to ensure the intra cluster compactness and inter cluster separation. Experiment on 20 synthetic datasets and 10 real-world datasets was conducted. The result shows that the proposed SHCA algorithm outperforms existing methods in clustering performance; In cases where performance degradation is observed on certain datasets,the analysis reveals that manifold overlap is the primary contributing factor. Overall, SHCA significantly enhances clustering accuracy and result stability, providing some reference for subsequent research on clustering of large-scale and complex manifold data.