Advanced high-throughput chromosome conformation capture techniques, like Hi-C, reveal genome organization into structural units like topologically associating domains (TADs), which are crucial in gene expression regulation. While accurately identifying TADs is vital, distinguishing different types of TAD boundaries and TAD categories remains a significant challenge in genomic research. We develop a Markov clustering-based tool, Mactop, to accurately identify TADs and provide biologically important classifications of TADs and their boundaries. Mactop distinguishes stable and dynamic boundaries based on biological significance. Mactop shows superior performance against multiple TAD-calling methods. More importantly, leveraging spatial interactions among TADs, Mactop uncovers TAD communities characterized by chromatin accessibility and enriched histone modifications. Mactop unveils the 'chromunity' within TADs in high-order interaction data, showing that interactions within TADs are diverse rather than uniform. In short, Mactop is a versatile, accurate, robust tool for deciphering chromatin domain, domain community, and chromunity for 3D genome maps.