When　processing　data　with　complex　spatial　attributes,　it　is　easy　to　fall　into　local　optima,　resulting　in　inaccurate　outlier　detection　results.　A　spatial　attribute　data　outlier　detection　method　is　proposed　using　the　artificial　bee　colony　algorithm　in　artificial　intelligence　technology　to　address　this　issue.　Construct　key　value　pairs　of　data　blocks,　calculate　the　fitness　of　the　solution,　use　greedy　selection　method　to　identify　the　center　points　of　each　cluster,　and　achieve　a　set　of　outliers　in　spatial　attribute　data.　Based　on　the　determined　center　points　of　each　cluster,　classify　the　outlier　attributes　of　the　set　space　attribute　data,　calculate　the　detection　radius,　and　identify　outliers　in　the　subset.　Simulate　the　foraging　behavior　of　bees,　calculate　the　local　reachable　density　of　outliers,　and　obtain　better　outlier　solutions　through　iterative　updates.　Calculate　the　outlier　factor　corresponding　to　the　object　and　detect　outlier　points.　The　simulation　results　indicate　that　the　proposed　method　achieves　a　detection　accuracy　of　90%　when　detecting　outliers,　providing　a　new　and　effective　tool　for　spatial　data　analysis.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2025.