The　construction　of　large-scale　civil　infrastructures　requires　massive　spatiotemporal　data　to　support　the　management　and　control　of　scheduling,　quality　control,　and　safety　monitoring.　Existing　artificial-intelligence-based　data　processing　algorithms　rely　heavily　on　experienced　engineers　to　adjust　the　parameters　of　data　processing,　which　is　inefficient　and　time-consuming　when　dealing　with　huge　datasets.　Limited　studies　have　compared　the　performance　of　different　algorithms　on　a　unified　dataset.　This　study　proposes　a　framework　and　evaluation　system　for　comparing　different　data　processing　policies　for　processing　huge　spatiotemporal　data　in　construction　quality　control.　The　proposed　method　compares　the　combination　of　multiple　types　of　algorithms　involved　in　the　processing　of　massive　point　cloud　data.　The　performance　of　data　processing　strategies　is　evaluated　through　this　framework,　and　the　optimal　point　cloud　processing　strategies　are　explored　based　on　registration　accuracy　and　data　fidelity.　Results　show　that　a　reasonable　choice　of　combinations　of　point　cloud　sampling,　filtering,　and　registration　algorithms　can　significantly　improve　the　efficiency　of　point　cloud　data　processing　and　satisfy　engineering　demands　for　data　accuracy　and　completeness.　The　proposed　method　can　be　applied　to　the　civil　engineering　problem　of　processing　a　large　amount　of　point　cloud　data　and　selecting　the　optimal　processing　method.