Architecture　is　a　representative　of　a　city.　It　is　also　a　spatial　carrier　of　urban　culture.　Identifying　the　architectural　features　in　a　city　can　help　with　urban　transformation　and　promote　urban　development.　The　use　of　visual　saliency　models　in　regional　architectural　texture　recognition　can　effectively　enhance　the　effectiveness　of　regional　architectural　texture　recognition.　In　this　paper,　the　improved　visual　saliency　model　first　enhances　the　texture　images　of　regional　buildings　through　histogram　enhancement　technology,　and　uses　visual　saliency　algorithms　to　extract　the　visual　saliency　of　the　texture　features　of　regional　buildings.　Then,　combined　with　the　maximum　interclass　difference　method　of　threshold　segmentation,　the　visual　saliency　image　is　segmented　to　achieve　accurate　target　recognition.　Finally,　the　feature　factor　iteration　of　the　Bag　of　Visual　Words　model　and　the　function　classification　of　support　vector　machines　were　used　to　complete　the　recognition　of　regional　architectural　texture　features.　Through　experimental　verification,　the　constructed　regional　architectural　texture　feature　recognition　method　based　on　visual　saliency　model　can　effectively　enhance　the　recognition　image.　This　method　performs　well　in　boundary　contour　separation　and　visual　saliency,　with　an　average　recognition　rate　of　0.814　for　texture　features　in　different　building　scenes,　indicating　high　stability.