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The K-nearest Neighbor (kNN) Algorithm Is A Classic Supervised Machine Learning Algorithm. It Is Widely Used In Cyber-physical-social Systems (CPSS) To Analyze And Mine Data. However, In Practical CPSS Applications, The Standard Linear KNN Algorithm Struggles To Efficiently Process Massive Data Sets. This Paper Proposes A Distributed Storage And Computation K-nearest Neighbor (D-kNN) Algorithm. The D-kNN Algorithm Has The Following Advantages: First, The Concept Of K-nearest Neighbor Boundaries Is Proposed And The K-nearest Neighbor Search Within The K-nearest Neighbors Boundaries Can Effectively Reduce The Time Complexity Of KNN. Second, Based On The K-neighbor Boundary, Massive Data Sets Beyond The Main Storage Space Are Stored On Distributed Storage Nodes. Third, The Algorithm Performs K-nearest Neighbor Searching Efficiently By Performing Distributed Calculations At Each Storage Node. Finally, A Series Of Experiments Were Performed To Verify The Effectiveness Of The D-kNN Algorithm. The Experimental Results Show That The D-kNN Algorithm Based On Distributed Storage And Calculation Effectively Improves The Operation Efficiency Of K-nearest Neighbor Search. The Algorithm Can Be Easily And Flexibly Deployed In A Cloud-edge Computing Environment To Process Massive Data Sets In CPSS.