For　hierarchical　learning,　one　open　issue　is　how　to　build　a　reasonable　hierarchical　structure　which　characterize　the　inter-relation　between　categories.　An　effective　approach　is　to　utilize　hierarchical　clustering　to　build　a　visual　tree　structure,　however,　the　critical　issue　of　this　approach　is　how　to　determine　the　number　of　clusters　in　hierarchical　clustering.　In　this　paper,　a　hierarchical　cluster　validity　index　(HCVI)　is　developed　for　supporting　visual　tree　learning.　Before　clustering　of　each　level　begins,　we　will　measure　the　impact　of　different　numbers　of　clusters　on　visual　tree　building　and　select　the　most　suitable　number　of　clusters.　The　proposed　HCVI　will　control　the　structure　of　visual　tree　neither　too　flat　nor　too　deep.　Based　on　this　visual　tree,　a　hierarchical　classifier　can　be　trained　for　achieving　more　discriminative　capability.　Our　experimental　results　have　demonstrated　that　the　proposed　hierarchical　cluster　validity　index　(HCVI)　can　guide　the　building　of　a　more　reasonable　visual　tree　structure,　so　that　the　hierarchical　classifier　can　achieve　better　results　on　classification　accuracy.　©　Springer　Nature　Switzerland　AG　2018.