After　deep　learning　algorithms　were　proposed,　artificial　intelligence　technology　applications　have　achieved　breakthrough　development.　Currently,　the　explosive　growth　of　data　provides　sufficient　nutrients　for　artificial　intelligence,　and　deep　learning　algorithms　have　achieved　breakthroughs　in　speech　and　visual　recognition,　making　it　possible　for　artificial　intelligence　industries　to　land　and　commercialize.　With　the　continuous　development　of　big　data,　deep　learning　and　cloud　computing,　we　can　obtain　more　and　richer　data,　develop　more　efficient　algorithms,　and　have　more　powerful　computing　power,　laying　the　foundation　for　another　artificial　intelligence　research　boom.　However,　with　the　application　of　AI-related　technologies　in　industries　such　as　information,　social　governance,　and　transportation,　the　problems　and　challenges　of　algorithmic　collusion　and　algorithmic　discrimination　have　gradually　emerged.　The　operating　principles　of　algorithms　differ　from　the　risk　of　algorithmic　collusion　that　may　result,　and　they　also　pose　different　degrees　of　regulatory　challenges　for　antitrust　enforcement.　By　understanding　the　data-driven　competitive　model　of　the　market　under　the　influence　of　algorithms,　the　efficient　information　interaction　mechanism　and　the　new　features　embodied　in　the　evolution　of　machine-driven　competition　can　prevent　the　breeding　of　technological　monopolies.　The　impact　of　algorithms　on　the　collusion　problem　has　two　dimensions:　the　first　dimension　is　to　change　the　environment　of　collusion;　the　second　dimension　is　to　be　applied　directly　as　a　tool　in　the　collusion　process.　This　article　attempts　to　analyze　the　challenges　brought　to　modern　market　competition　by　exploring　the　state　of　artificial　intelligence　technology　that　causes　data　monopoly.　Thus,　In　order　to　better　regulate　the　evaluation　and　regulation　of　artificial　intelligence　algorithm　collusion,　this　article　proposes　related　solutions　based　on　the　Chinese　perspective,　including:　1)　broaden　the　extension　of　the　competitive　relationship　and　identify　the　subject　of　monopolistic　behavior　in　accordance　with　the　idea　of　cooperative　behavior;　2)　increase　platform　factors　to　identify　market　dominance;　3)　use　case　analysis　as　the　main　method　to　define　relevant　markets.　©　2022　SPIE.